University of Isfahan Geography and Environmental Planning 2008-5362 33 2 2022 06 22 Investigating the Effect of Saffron Plant Age on Its Production in Lorestan Province Investigating the Effect of Saffron Plant Age on Its Production in Lorestan Province 1 18 26146 10.22108/gep.2021.129837.1445 FA Haniyeh Nazaripour PhD Student in Climatology, Department of Natural Geography, Faculty of Geography and Planning, University of Isfahan, Isfahan, Iran Javad Khoshhal Dastjerdi Associate Professor of Climatology, Department of Natural Geography, Faculty of Geography and Planning, University of Isfahan, Isfahan, Iran Ali Baratian Ph.D. in Climatology, University of Isfahan, Isfahan, Iran Journal Article 2021 08 04 Abstract Saffron is a very popular medicinal plant and the most expensive spice in the world. It is highly considered in traditional medicine for the treatment of varied diseases. From among Iranian agricultural crops, it is one of the most valuable products. Due to its special characteristics, its production and export can be developed. As the largest producer and exporter of saffron in the world, Iran accounts for more than 90% of global saffron production. In 2018, more than 71% of the global export of saffron belonged to Iran. Lorestan Province is becoming one of the important areas for cultivating this crop due to its natural conditions and farmers’ interest. This study tried to identify the factors affecting the sustainable development of this crop. For this purpose, during the two consecutive years of 2017 and 18, 3 regions in Kuhdasht, Kuhnani, and Khorramabad townships were selected, in which 5 sample farms were chosen based on the field research. The selected farms had a planting history of 1 to 5 years. Several quadrants were established in the farms and the phenological measurements were recorded based on them. The statistics of these farms were collected to determine the required yields. The means and ANOVA comparisons were applied to analyze the results. The studied parameter was farm age from 1 to 5 years in the 3 regions of Kuhdasht, Kuhnani, and Khorramabad townships. In this investigation, saffron flower characteristics and dry weights of stigma were determined simultaneously with daily phenological inspections of the farms. The results in both years revealed that age was the major factor in yield change in all the 3 regions in a way that saffron yield increased with increasing farm age up to 4 years and then decreased. The effects of farm age on saffron flower and stigma yields were statistically significant. It was found that the lowest and highest yields were respectively related to the 1-, 2-, and 5-year-old farms and 3-year-old farms with a peak yield for the 4-year-old farms. During the study of the role of temperature in the two consecutive years, it was observed that the average temperature had been higher in 2017 and thus the flowering duration had decreased. Accordingly, saffron yield in this year compared to 2018 showed a smaller value, which indicated the role of temperature in yield enhancement during the flowering period. Yield differences were significant in the 3 studied regions. The highest and lowest yields were related to Kuhnani and Khorramabad farms, respectively. Keywords: saffron, Lorestan, farm age, yield, comparison of means Introduction: As a very popular medicinal plant, saffron, is the most expensive spice in the world. It is highly regarded in traditional medicine for being used in the treatment of some diseases. Among Iranian agricultural products, it is one of the most valuable plants since its production and export can be expanded based on its special characteristics. Iran is the largest producer and exporter of saffron in the world and accounts for more than 90% of global saffron production. In 2018, more than 71% of the global export of saffron belonged to Iran. Lorestan Province is located in the west of Iran and has more potential for the production of agricultural products due to its environmental conditions, especially climate, when compared to other eastern, southern, and central provinces. In recent decades, farmers in different parts of this province like other provinces have been more interested in cultivating this crop due to the occurrence of drought and lack of water for agriculture on the one hand and its valuable characteristics on the other hand. Therefore, this province is becoming one of the important areas for farming saffron because of the dominating natural conditions and farmers’ increasing interest in its cultivation. Methodology: This research was conducted in the 3 townships of Kuhdasht, Kuhnani, and Khorramabad in Lorestan Province through field and laboratory methods in 2017 and 2018. Aphenological monitoring site was created in the vicinity of synoptic meteorological stations located in the mentioned townships upon the recommendation of the Agricultural Research Center of the province and the farmers, who had grown saffron bulbs on their farms at the first year in 2013, 2014, 2015, and 2016 and whose plants were still continuing their biological activities. They were invited to cooperate with the researcher at the time of dormancy of their plants or corms in mid-spring (late May) in 2017. Also, another farm, on which saffron plant was to be cultivated in 2017, was planned to be equipped with the mentioned monitoring site. In the middle of May,2018, the farmers removed the saffron corms or bulbs planted on their farms in 2013 for sale and subsequent crop rotation. To replace any farms excluded from the research, a new farm was designated in the selected area in each township and the monitoring was continued. In these farms, the cultivated plants, which had lived from 1 to 5 years, were examined in terms of their phenological stages during the two years of monitoring. In the selected farms, they were divided into equal parts and a code was given to each part. Then, from among the codes, 3 codes were randomly selected for creating a quadrant. Monitoring of the phenological phases was performed based on the BBCH coding system at the same time as recording the relevant weather conditions. In this way, 15 quadrants were created, in which the plants aging 1-5 years could be monitored. Monitoring was continued on the same farms in the second year as well. Discussion: In both years, the results revealed that age was the most important factor in yield change in all the 3 regions. With the increasing ages of the farms up to 4 years, saffron yield showed an upward trend and then decreased. Its flower and stigma yields were low during the first years of cultivation in all the 3 study regions in the two consecutive years. With increasing farm age up to 4 years, saffron yield had an upward trend and reached its peak. Then, the flower and stigma productions decreased until 5 years of farm age. The lowest and highest yields were respectively related to the 1-, 2-, and 5-year-old farms and the 3-year-old farms with a peak in the 4th year. Saffron yields were affected by the natural conditions of the regions and farm age. The total Growing Degree-Days (GDDs) were calculated according to the effective and active temperatures and growth period lengths in Kuhdasht, Kuhnani, and Khorramabad townships during the two years of research. In the first year, the plants in the farms of the mentioned townships had 213, 239, and 204 GDDs and 333, 360, and 314 GDDs based on the active and effective temperatures, respectively. These GDDs were achieved within 24, 24, and 22 days, respectively. In the second year, these farms obtained 190, 263, and 186 GDDs and 327, 371, and 306 GDDs based on the mentioned temperatures, respectively. These GDDs were achieved within 26, 27, and 24 days, respectively. Saffron is a cold-loving plant and its activity and growth period start as the weather begins to get relatively cold. During the study on the role of temperature in the two consecutive years, it was observed that the average temperature was higher in 2017, thus reducing flowering duration. Saffron yield showed a lower amount in this year compared to 2018, which indicated the role of temperature in the flowering period. Conclusion: The results of this research demonstrated that age was the major factor in yield change in a way that saffron yield had an upward trend up to 4 years and then decreased with increasing farm age. Also, the highest flower and stigma yields were observed in the 4-year-old farms, while flower yield decreased with increasing the farm age up to 5 years. In addition, increasing farm age caused enhanced dry weights of the corms. In the first years of saffron cultivation, saffron flower and stigma yields were low and saffron yield was augmented as farm age increased up to 4 years, thus reaching its peak. Afterwards, the amounts of flower and stigma productions per unit area lowered with increasing farm age so that the lowest and highest yields were respectively obtained from the 1-, 2-, and 5-year-old farms and the 3-year-old farms with a peak in the 4-year-old farms. Saffron flower and stigma yields were influenced by the natural conditions of the regions and farm age. Based on the data analysis, the differences in the flower and stigma yields in the 3 studied regions were significant. The highest and lowest yields were related to the farms of Kuhnani and Khorramabad townships, respectively. The effects of farm age on saffron flower and stigma yields were significant as well. Analysis of Variance allows checking certainty of the existence of a linear relationship between variables. Since the levels of significance in the mentioned townships were less than 5%, a significant relationship between farm age and saffron yield could be deduced. Comparison of the sums of squares within the groups and outside the groups of the 3 studied regions demonstrated that the sum of squares within the groups had a smaller share in the total dispersion and thus, the assumption that the yields in the regions were the same was rejected. The GDDs in terms of effective and active temperatures and the growth period length were calculated during the two years of research in the townships. According to the calculation results, 213, 239, and 204 GDDs and 333, 360, and 314 GDDs were obtained based on the active and effective temperatures in the farms of Kuhdasht, Kuhnani, and Khorramabad townships in the first year of the plant cultivation, respectively. These GDDs were achieved within 24, 24, and 22 days, respectively. In the second year, the farms of the mentioned townships obtained 190, 236, and 186 GDDs and 327, 371, and 306 GDDs based on the mentioned temperatures, respectively. The GDDs were achieved within 26, 27, and 24 days, respectively. Saffron is a cold-loving plant and its activity and growth period start as the weather begins to get relatively cold. During the study on the role of temperature in the two consecutive years, the average temperature was observed to be higher in 2017, thus alleviating the flowering duration. A lower value of saffron yield was evidenced in this year compared to 2018, which was indicative of the role of temperature in yield enhancement during the flowering period. References - Bazrafshan, O., Ramezani Etedali, H., Gerkani Nezhad Moshizi, Z., & Shamili, M. (2019). Virtual water trade and water footprint accounting of saffron production in Iran. Agricultural Water Management, 213(5) 368-374. https://doi.org/10.1016/j.agwat.2018.10.034 - El Hajj, A., Moustafa, S., Oleik, S,. Telj1, V., Taha, N. Chehabeldine, H., & El Tachach, T.(2019). Yield of Saffron (Crocus sativus) under Different Corm Densities. Journal of Agricultural Science, Vol. 11, No. 8, 2019. ISSN 1916-9752 E-ISSN 1916-9760. URL: https://doi.org/10.5539/jas.v11n8p183 - Ferrara, L., Naviglio, D., Gallo, M. (2014). Extraction of Bioactive Compounds of Saffron (Crocus sativus L.) by Ultrasound Assisted Extraction (UAE) and by Rapid Solid-Liquid Dynamic Extraction (RSLDE). European Scientific Journal, 10(3), 1-13. https://www.researchgate.net/publication/259997094 - KUMAR, R., SINGH, V., DEVI, K., SHARMA, M., SINGH, M.K., & AHUJA, P.S.(2009). State of Art of Saffron (Crocus sativus L.) Agronomy: A Comprehensive Review. Article (January 2009). DOI: 10.1080/87559120802458503. https://www.researchgate.net/publication/224873548 - Kothari, D., Thakur, M., Joshi, R., Kumar, A.& Kumar, R. (2021).Agro-Climatic Suitability Evaluation for Saffron Production in Areas of Western Himalaya. Published: 15 March, 2021. doi: 10.3389/fpls.2021.657819. https://doi.org/10.3389/fpls.2021.65781 - Lopez-Corcoles, H., Brasa-Ramos, A., Montero-García, F., Romero-Valverde, M., & Montero-Riquelme, F. (2015). Phenological growth stages of saffron plant (Crocus sativus L.) according to the BBCH Scale. Spanish Journal of Agricultural Research. 13(3), e09SC01, 7 pages (2015). http://dx.doi.org/10.5424/sjar/2015133-7340 - Mohammadi, H. (2015). Effects of corm size and plant density on Saffron (Crocus sativus L.) yield and its components. International Journal of Agronomy and Agricultural Research (IJAAR), Vol. 6, No. 3, pp. 20-26, 2015.https://www.researchgate.net/publication/306118724 - Menia, M., Iqbal, S., Zahida, R., Tahir, S., Kanth, R. H., Saad, A. A., & Hussian, A. (2018). Production technology of saffron for enhancing productivity. Journal of Pharmacognosy and Phytochemistry (2018), 7(1): 1033-1039. Available online at www.Phytojournal.com - Temperini, O., Rea, R., Temperini, A., Colla, G., & Rouphael, Y. Evaluation of saffron (Crocus sativus L.) production in Italy: Effects of the age of saffron fields and plant density.Journal of Food, Agriculture & Environment, Vol.7(1): 1 9-2 3,2 0 0 9. https://www.researchgate.net/publication/268268074 - Sabet Temouria, M. Investigation of planting age farm on saffron characteristics and corm position in soil, Kashmar, Iran. Article inActa Horticulturae, November, 2017. https://www.researchgate.net/publication/321366652 - Sepaskhah, A. R. and Kamgar-Haghighi, A. A. (2009). Saffron Irrigation Regime. Journal of production. International Journal of Plant Production, Vol. 3, 3(1), January, 2009 .ISSN: 1735-6814 (Print), 1735-8043 (Online) .This is a refereed journal and all articles are professionally screened and reviewed. - Fig. 1: Location of the studied cities - Table 1: Geographical characteristics of the study areas - Table 2: Average temperature, average minimum and maximum temperatures, and precipitation in the study areas in 2017-2018 - Fig. 2: Graph of average monthly temperatures of the stations in 2017 and 2018 - Fig. 3: Graph of average monthly rainfalls of the stations in 2017 and 2018 - Fig. 2: Locations of the selected farms by age in the cities of Kuhnani, Kuhdasht, and Khorramabad in 2017-2018 - Table 3: Comparison of means in all the 3 study areas in 2017-2018 - Table 4: Beginning and ending dates, means of minimum and maximum temperatures, and mean daily temperature during the flowering phase in the study areas in 2017 - Table 5: Beginning and ending dates, means of minimum and maximum temperatures, and mean daily temperature during the flowering phase in the study areas in 2018 - Table 6: Saffron yield (g/ha) in terms of plant age in the studied areas in 2017-2018 - Fig. 5: Diagram of saffron crop yield in the studied areas in 2017 - Fig. 6: Diagram of saffron crop yield in the studied areas in 2018 Abstract Saffron is a very popular medicinal plant and the most expensive spice in the world. It is highly considered in traditional medicine for the treatment of varied diseases. From among Iranian agricultural crops, it is one of the most valuable products. Due to its special characteristics, its production and export can be developed. As the largest producer and exporter of saffron in the world, Iran accounts for more than 90% of global saffron production. In 2018, more than 71% of the global export of saffron belonged to Iran. Lorestan Province is becoming one of the important areas for cultivating this crop due to its natural conditions and farmers’ interest. This study tried to identify the factors affecting the sustainable development of this crop. For this purpose, during the two consecutive years of 2017 and 18, 3 regions in Kuhdasht, Kuhnani, and Khorramabad townships were selected, in which 5 sample farms were chosen based on the field research. The selected farms had a planting history of 1 to 5 years. Several quadrants were established in the farms and the phenological measurements were recorded based on them. The statistics of these farms were collected to determine the required yields. The means and ANOVA comparisons were applied to analyze the results. The studied parameter was farm age from 1 to 5 years in the 3 regions of Kuhdasht, Kuhnani, and Khorramabad townships. In this investigation, saffron flower characteristics and dry weights of stigma were determined simultaneously with daily phenological inspections of the farms. The results in both years revealed that age was the major factor in yield change in all the 3 regions in a way that saffron yield increased with increasing farm age up to 4 years and then decreased. The effects of farm age on saffron flower and stigma yields were statistically significant. It was found that the lowest and highest yields were respectively related to the 1-, 2-, and 5-year-old farms and 3-year-old farms with a peak yield for the 4-year-old farms. During the study of the role of temperature in the two consecutive years, it was observed that the average temperature had been higher in 2017 and thus the flowering duration had decreased. Accordingly, saffron yield in this year compared to 2018 showed a smaller value, which indicated the role of temperature in yield enhancement during the flowering period. Yield differences were significant in the 3 studied regions. The highest and lowest yields were related to Kuhnani and Khorramabad farms, respectively. Keywords: saffron, Lorestan, farm age, yield, comparison of means Introduction: As a very popular medicinal plant, saffron, is the most expensive spice in the world. It is highly regarded in traditional medicine for being used in the treatment of some diseases. Among Iranian agricultural products, it is one of the most valuable plants since its production and export can be expanded based on its special characteristics. Iran is the largest producer and exporter of saffron in the world and accounts for more than 90% of global saffron production. In 2018, more than 71% of the global export of saffron belonged to Iran. Lorestan Province is located in the west of Iran and has more potential for the production of agricultural products due to its environmental conditions, especially climate, when compared to other eastern, southern, and central provinces. In recent decades, farmers in different parts of this province like other provinces have been more interested in cultivating this crop due to the occurrence of drought and lack of water for agriculture on the one hand and its valuable characteristics on the other hand. Therefore, this province is becoming one of the important areas for farming saffron because of the dominating natural conditions and farmers’ increasing interest in its cultivation. Methodology: This research was conducted in the 3 townships of Kuhdasht, Kuhnani, and Khorramabad in Lorestan Province through field and laboratory methods in 2017 and 2018. Aphenological monitoring site was created in the vicinity of synoptic meteorological stations located in the mentioned townships upon the recommendation of the Agricultural Research Center of the province and the farmers, who had grown saffron bulbs on their farms at the first year in 2013, 2014, 2015, and 2016 and whose plants were still continuing their biological activities. They were invited to cooperate with the researcher at the time of dormancy of their plants or corms in mid-spring (late May) in 2017. Also, another farm, on which saffron plant was to be cultivated in 2017, was planned to be equipped with the mentioned monitoring site. In the middle of May,2018, the farmers removed the saffron corms or bulbs planted on their farms in 2013 for sale and subsequent crop rotation. To replace any farms excluded from the research, a new farm was designated in the selected area in each township and the monitoring was continued. In these farms, the cultivated plants, which had lived from 1 to 5 years, were examined in terms of their phenological stages during the two years of monitoring. In the selected farms, they were divided into equal parts and a code was given to each part. Then, from among the codes, 3 codes were randomly selected for creating a quadrant. Monitoring of the phenological phases was performed based on the BBCH coding system at the same time as recording the relevant weather conditions. In this way, 15 quadrants were created, in which the plants aging 1-5 years could be monitored. Monitoring was continued on the same farms in the second year as well. Discussion: In both years, the results revealed that age was the most important factor in yield change in all the 3 regions. With the increasing ages of the farms up to 4 years, saffron yield showed an upward trend and then decreased. Its flower and stigma yields were low during the first years of cultivation in all the 3 study regions in the two consecutive years. With increasing farm age up to 4 years, saffron yield had an upward trend and reached its peak. Then, the flower and stigma productions decreased until 5 years of farm age. The lowest and highest yields were respectively related to the 1-, 2-, and 5-year-old farms and the 3-year-old farms with a peak in the 4th year. Saffron yields were affected by the natural conditions of the regions and farm age. The total Growing Degree-Days (GDDs) were calculated according to the effective and active temperatures and growth period lengths in Kuhdasht, Kuhnani, and Khorramabad townships during the two years of research. In the first year, the plants in the farms of the mentioned townships had 213, 239, and 204 GDDs and 333, 360, and 314 GDDs based on the active and effective temperatures, respectively. These GDDs were achieved within 24, 24, and 22 days, respectively. In the second year, these farms obtained 190, 263, and 186 GDDs and 327, 371, and 306 GDDs based on the mentioned temperatures, respectively. These GDDs were achieved within 26, 27, and 24 days, respectively. Saffron is a cold-loving plant and its activity and growth period start as the weather begins to get relatively cold. During the study on the role of temperature in the two consecutive years, it was observed that the average temperature was higher in 2017, thus reducing flowering duration. Saffron yield showed a lower amount in this year compared to 2018, which indicated the role of temperature in the flowering period. Conclusion: The results of this research demonstrated that age was the major factor in yield change in a way that saffron yield had an upward trend up to 4 years and then decreased with increasing farm age. Also, the highest flower and stigma yields were observed in the 4-year-old farms, while flower yield decreased with increasing the farm age up to 5 years. In addition, increasing farm age caused enhanced dry weights of the corms. In the first years of saffron cultivation, saffron flower and stigma yields were low and saffron yield was augmented as farm age increased up to 4 years, thus reaching its peak. Afterwards, the amounts of flower and stigma productions per unit area lowered with increasing farm age so that the lowest and highest yields were respectively obtained from the 1-, 2-, and 5-year-old farms and the 3-year-old farms with a peak in the 4-year-old farms. Saffron flower and stigma yields were influenced by the natural conditions of the regions and farm age. Based on the data analysis, the differences in the flower and stigma yields in the 3 studied regions were significant. The highest and lowest yields were related to the farms of Kuhnani and Khorramabad townships, respectively. The effects of farm age on saffron flower and stigma yields were significant as well. Analysis of Variance allows checking certainty of the existence of a linear relationship between variables. Since the levels of significance in the mentioned townships were less than 5%, a significant relationship between farm age and saffron yield could be deduced. Comparison of the sums of squares within the groups and outside the groups of the 3 studied regions demonstrated that the sum of squares within the groups had a smaller share in the total dispersion and thus, the assumption that the yields in the regions were the same was rejected. The GDDs in terms of effective and active temperatures and the growth period length were calculated during the two years of research in the townships. According to the calculation results, 213, 239, and 204 GDDs and 333, 360, and 314 GDDs were obtained based on the active and effective temperatures in the farms of Kuhdasht, Kuhnani, and Khorramabad townships in the first year of the plant cultivation, respectively. These GDDs were achieved within 24, 24, and 22 days, respectively. In the second year, the farms of the mentioned townships obtained 190, 236, and 186 GDDs and 327, 371, and 306 GDDs based on the mentioned temperatures, respectively. The GDDs were achieved within 26, 27, and 24 days, respectively. Saffron is a cold-loving plant and its activity and growth period start as the weather begins to get relatively cold. During the study on the role of temperature in the two consecutive years, the average temperature was observed to be higher in 2017, thus alleviating the flowering duration. A lower value of saffron yield was evidenced in this year compared to 2018, which was indicative of the role of temperature in yield enhancement during the flowering period. References - Bazrafshan, O., Ramezani Etedali, H., Gerkani Nezhad Moshizi, Z., & Shamili, M. (2019). Virtual water trade and water footprint accounting of saffron production in Iran. Agricultural Water Management, 213(5) 368-374. https://doi.org/10.1016/j.agwat.2018.10.034 - El Hajj, A., Moustafa, S., Oleik, S,. Telj1, V., Taha, N. Chehabeldine, H., & El Tachach, T.(2019). Yield of Saffron (Crocus sativus) under Different Corm Densities. Journal of Agricultural Science, Vol. 11, No. 8, 2019. ISSN 1916-9752 E-ISSN 1916-9760. URL: https://doi.org/10.5539/jas.v11n8p183 - Ferrara, L., Naviglio, D., Gallo, M. (2014). Extraction of Bioactive Compounds of Saffron (Crocus sativus L.) by Ultrasound Assisted Extraction (UAE) and by Rapid Solid-Liquid Dynamic Extraction (RSLDE). European Scientific Journal, 10(3), 1-13. https://www.researchgate.net/publication/259997094 - KUMAR, R., SINGH, V., DEVI, K., SHARMA, M., SINGH, M.K., & AHUJA, P.S.(2009). State of Art of Saffron (Crocus sativus L.) Agronomy: A Comprehensive Review. Article (January 2009). DOI: 10.1080/87559120802458503. https://www.researchgate.net/publication/224873548 - Kothari, D., Thakur, M., Joshi, R., Kumar, A.& Kumar, R. (2021).Agro-Climatic Suitability Evaluation for Saffron Production in Areas of Western Himalaya. Published: 15 March, 2021. doi: 10.3389/fpls.2021.657819. https://doi.org/10.3389/fpls.2021.65781 - Lopez-Corcoles, H., Brasa-Ramos, A., Montero-García, F., Romero-Valverde, M., & Montero-Riquelme, F. (2015). Phenological growth stages of saffron plant (Crocus sativus L.) according to the BBCH Scale. Spanish Journal of Agricultural Research. 13(3), e09SC01, 7 pages (2015). http://dx.doi.org/10.5424/sjar/2015133-7340 - Mohammadi, H. (2015). Effects of corm size and plant density on Saffron (Crocus sativus L.) yield and its components. International Journal of Agronomy and Agricultural Research (IJAAR), Vol. 6, No. 3, pp. 20-26, 2015.https://www.researchgate.net/publication/306118724 - Menia, M., Iqbal, S., Zahida, R., Tahir, S., Kanth, R. H., Saad, A. A., & Hussian, A. (2018). Production technology of saffron for enhancing productivity. Journal of Pharmacognosy and Phytochemistry (2018), 7(1): 1033-1039. Available online at www.Phytojournal.com - Temperini, O., Rea, R., Temperini, A., Colla, G., & Rouphael, Y. Evaluation of saffron (Crocus sativus L.) production in Italy: Effects of the age of saffron fields and plant density.Journal of Food, Agriculture & Environment, Vol.7(1): 1 9-2 3,2 0 0 9. https://www.researchgate.net/publication/268268074 - Sabet Temouria, M. Investigation of planting age farm on saffron characteristics and corm position in soil, Kashmar, Iran. Article inActa Horticulturae, November, 2017. https://www.researchgate.net/publication/321366652 - Sepaskhah, A. R. and Kamgar-Haghighi, A. A. (2009). Saffron Irrigation Regime. Journal of production. International Journal of Plant Production, Vol. 3, 3(1), January, 2009 .ISSN: 1735-6814 (Print), 1735-8043 (Online) .This is a refereed journal and all articles are professionally screened and reviewed. - Fig. 1: Location of the studied cities - Table 1: Geographical characteristics of the study areas - Table 2: Average temperature, average minimum and maximum temperatures, and precipitation in the study areas in 2017-2018 - Fig. 2: Graph of average monthly temperatures of the stations in 2017 and 2018 - Fig. 3: Graph of average monthly rainfalls of the stations in 2017 and 2018 - Fig. 2: Locations of the selected farms by age in the cities of Kuhnani, Kuhdasht, and Khorramabad in 2017-2018 - Table 3: Comparison of means in all the 3 study areas in 2017-2018 - Table 4: Beginning and ending dates, means of minimum and maximum temperatures, and mean daily temperature during the flowering phase in the study areas in 2017 - Table 5: Beginning and ending dates, means of minimum and maximum temperatures, and mean daily temperature during the flowering phase in the study areas in 2018 - Table 6: Saffron yield (g/ha) in terms of plant age in the studied areas in 2017-2018 - Fig. 5: Diagram of saffron crop yield in the studied areas in 2017 - Fig. 6: Diagram of saffron crop yield in the studied areas in 2018

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University of Isfahan Geography and Environmental Planning 2008-5362 33 2 2022 06 22 Frequency Analysis and Investigation of the Factors Affecting 100-yr Peak-Flood in Iran’s Watersheds Frequency Analysis and Investigation of the Factors Affecting 100-yr Peak-Flood in Iran’s Watersheds 19 38 26367 10.22108/gep.2022.130040.1450 FA ٍEsmaeel Parizi Postdoctoral Researcher, Department of Physical Geography, Faculty of Natural Geography, University of Tehran, Tehran, Iran Seiyed Mossa Hosseini Associate Professor, Department of Physical Geography, Faculty of Natural Geography, University of Tehran, Tehran, Iran Journal Article 2021 08 21 Abstract The purpose of the current study is to analyze the frequency of peak flood discharge with a 100-year return period in 206 Iran watersheds and to quantify it based on the most important factors. In this regard, flood frequency analysis was performed based on annual maximum discharge data and fitting of conventional continuous distributions in hydrology and fitting statistical tests. Then, for modeling, 8 parameters affecting the flood peak discharge including heavy daily rainfall, average vegetation, area, perimeter, average slope, average elevation, length of the main river, and the slope of the main river at the catchment area leading to the extraction of selected hydrometric stations. Also, the stepwise regression analysis technique was used to determine the factors affecting the production of flood peak discharge in the selected stations. The results of the study showed that the southwestern, southern, and southeastern basins of Iran with peak discharges of more than 4000 m3/s had the highest 100-year peak discharges among the study basins. The results of the stepwise regression model indicated that five parameters of area, heavy rainfall, elevation, vegetation, and slope of the basin with an adjusted coefficient of determination of 0.72, standard error of estimation of 132.7, Akaike's information criterion of 1.62, and variance inflation factor of 0.62 had the best performance in estimating the flood peak discharge. The results of this study, considering its large spatial scale, which includes the whole of Iran, can be used as a practical guide by the hydrologists and decision-makers in estimating the 100-year flood peak discharge in ungauged watersheds based on the most important factors affecting its generations. Introduction Flood is one of the most important natural hazards that has attracted a lot of attention from managers and planners due to the heavy damage it has caused to human societies (Jahangir et al., 2019). In fact, floods, as a type of natural disaster, have a significant negative impact on regional development, and its catastrophes are characterized by sudden water flow, high intensity, uncontrollable factors, and serious damages (Miceli et al., 2008). On the other hand, among various types of natural disasters such as earthquakes, landslides, soil erosion, and tsunamis, floods are considered to be the most common and destructive phenomena of the earth that affect the lives of many people every year (Doocy et al., 2013; Salvati et al., 2018; Yari et al., 2019). High socio-economic losses, human casualties, widespread destruction, and threatening living conditions are some of the damages that floods can cause (Turgut & Tevfik, 2012). It can be stated that half of the deaths occur due to floods (FitzGerald et al., 2010; Lee & Vink, 2015). In recent years, Iran has experienced very destructive floods due to climate change and poor watershed management (deforestation, overgrazing, and lack of flood control measures). For example, the recent floods (2019) in Iran have affected 25 provinces, killed 77 people, and caused about $ 2.2 billion in damage to these 25 provinces (Khosravi et al., 2020). Methodology In the first step, the Iran hydrometric stations that had discharge data with maximum long-term annual peak records (at least 30 years) were collected from the Iran Water Resources Management Company. In the next step, flood frequency analysis was performed based on the fitting of conventional continuous distributions in hydrology and fitting statistical tests. After performing flood frequency analysis and estimating peak discharge for 100 year return period, the watersheds boundary of hydrometric stations was determined. In this regard, using a digital elevation model with 12.5 m resolution and ARC GIS, Global Mapper, and Surfer software, the boundaries of the studied watersheds were extracted. Then, using the watersheds boundary and digital elevation model, the geomorphic parameters of the watershed such as perimeter, area, average slope, average elevation, length of the main river, and the slope of the main river were calculated. In the next step, long-term daily precipitation data of synoptic stations were collected from Iran Meteorological Organization. Then, 95% of the non-zero daily precipitation series was calculated for heavy precipitation (Gu et al., 2017). Using the IDW method, the long-term amount of heavy rainfall for each watershed was determined in GIS software. The NDVI index was used to determine the mean annual vegetation. In this regard, the vegetation time series for each watershed was extracted using Landsat images from 2000 to 2019 with a resolution of 30 m on the Google Earth Engine platform. After calculating the 100-year return period and possible parameters influencing the flood in the study watersheds, using Pearson bivariate analysis and stepwise regression model, the most suitable models for estimating flood peak discharge were determined. Discussion The results of the study show that the southwestern, southern, and southeastern watersheds of Iran with peak discharges of more than 4000 cubic meters per second have the highest peak discharges of 100 years among the study watersheds. Meanwhile, the Minab watershed, which ends in the Persian Gulf, has a maximum peak flow of 100 years with a peak flow of 12,614 cubic meters per second. On the other hand, the northwestern and northern watersheds of Iran with a peak discharge of less than 300 cubic meters per second have the lowest peak discharge, with a minimum discharge of 20.7 cubic meters per second related to the Solan watershed in Hamadan province. The findings of the stepwise regression model indicated that the five parameters of the watershed, including area, heavy rainfall, mean elevation, vegetation, and mean slope with R2 = 0.72 and significance level of 0.01, are the most influential factors in the estimation of flood peak discharge. In addition, the results showed that the three factors of watershed area, heavy rainfall, and mean slope have a direct relationship with peak discharge but mean elevation and vegetation have an inverse relationship. Conclusion This study quantified the relative contribution of driving factors influencing the flood peak discharge over 100 years across Iran. Considering its large spatial scale, which includes the whole of Iran, it can be used as a practical guide by the hydrologists and decision-makers in estimating the 100-year flood peak discharge in ungauged watersheds based on the most important factors affecting its generations. Keywords: Flood Peak Discharge, Modeling, Iran’s Watersheds, Stepwise Regression, Geomorphic Factors. References - Adhikari, P., Hong, Y., Douglas, K. R., Kirschbaum, D. B., Gourley, J., Adler, R., & Brakenridge, G. R. (2010). A digitized global flood inventory (1998–2008): Compilation and preliminary results. Journal of Natural Hazards, 55(2), 405–422. - Ahern, M., Kovats, R. S., Wilkinson, P., Few, R., & Matthies, F. (2005). Global health impacts of floods: Epidemiologic evidence. Journal of Epidemiologic Reviews, 27(1), 36–46. - Bennett, B., Leonard, M., Deng, Y., & Westra, S. (2018). An empirical investigation into the effect of antecedent precipitation on flood volume. Journal of Hydrology, 567, 435–445. - Chang, H. S., & Chen, T. L. (2016). Spatial heterogeneity of local flood vulnerability indicators within flood-prone areas in Taiwan. Journal of Environmental Earth Sciences, 75(23), 1-14. - Chau, K. W. (2017). Use of meta-heuristic techniques in rainfall-runoff modelling. Basel, Switzerland: Multidisciplinary Digital Publishing Institute. - Chuntian, C., & Chau, K. W. (2002). Three-person multi-objective conflict decision in reservoir flood control. European Journal of Operational Research, 142(3), 625–631. - Croke, J., Thompson, C., & Fryirs, K. (2017). Prioritising the placement of riparian vegetation to reduce flood risk and end-of-catchment sediment yields: Important considerations in hydrologically-variable regions. Journal of Environmental Management, 190, 9–19. - Doocy, S., Daniels, A., Packer, C., Dick, A., & Kirsch, T. D. (2013). The human impact of earthquakes: A historical review of events 1980-2009 and systematic literature review. Journal of Plos Currents, 5. - El-Hames, A. S. (2012). An empirical method for peak discharge prediction in ungauged arid and semi-arid region catchments based on morphological parameters and SCS curve number. Journal of Hydrology, 456, 94-100. - Ezemonye, M. N., & Emeribe, C. N. (2011). Flood characteristics and management adaptations in parts of the Imo river system. Ethiopian Journal of Environmental Studies and Management, 4(3), 56–64. - Falah, F., Rahmati, O., Rostami, M., Ahmadisharaf, E., Daliakopoulos, I. N., & Pourghasemi, H. R. (2019). Artificial neural networks for flood susceptibility mapping in data-scarce urban areas. In Spatial modeling in GIS and R for Earth and Environmental Sciences (pp. 323-336). Elsevier. - Feng, L. H., & Lu, J. (2010). The practical research on flood forecasting based on artificial neural networks. Journal of Expert Systems with Applications, 37(4), 2974–2977. - FitzGerald, G., Du, W., Jamal, A., Clark, M., & Hou, X. (2010). Flood fatalities in contemporary Australia (1997–2008). Journal of Emergency Medicine Australasia, 22(2), 180–186. - Ghavidel, Y., & Hombari, F. J. (2020). Synoptic analysis of unexampled super-heavy rainfall on April 1, 2019, in west of Iran. Journal of Natural Hazards, 104(2), 1567-1580. - Gu, X., Zhang, Q., Singh, V. P., & Shi, P. (2017). Changes in magnitude and frequency of heavy precipitation across China and its potential links to summer temperature. Journal of Hydrology, 547, 718–731. - Haghizadeh, A., Siahkamari, S., Haghiabi, A. H., & Rahmati, O. (2017). Forecasting flood-prone areas using Shannon’s entropy model. Journal of Earth System Science, 126(3), 1-11. - Haynes, K., Coates, L., van den Honert, R., Gissing, A., Bird, D., de Oliveira, F. D., … & Radford, D. (2017). Exploring the circumstances surrounding flood fatalities in Australia, 1900–2015 and the implications for policy and practice. Journal of Environmental Science & Policy, 76, 165–176. - Herget, J., Roggenkamp, T., & Krell, M. (2014). Estimation of peak discharges of historical floods. Journal of Hydrology and Earth System Sciences, 18(10), 4029. - Jahangir, M. H., Reineh, S. M. M., & Abolghasemi, M. (2019). Spatial predication of flood zonation mapping in Kan River Watershed, Iran, using artificial neural network algorithm. Journal of Weather and Climate Extremes, 25, 100215. - Khosravi, K., Panahi, M., Golkarian, A., Keesstra, S. D., Saco, P. M., Bui, D. T., & Lee, S. (2020). Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. Journal of Hydrology, 591, 125552. - Khosravi, K., Pourghasemi, H. R., Chapi, K., & Bahri, M. (2016). Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: A comparison between Shannon’s entropy, statistical index, and weighting factor models. Journal of Environmental Monitoring and Assessment, 188(12), 1-21. - Kirch, W., Menne, B., & Bertollini, R. (Eds.). (2005). Extreme weather events and public health responses. Berlin, Heidelberg: Springer Berlin Heidelberg. - Lashkari, H., Mohammadi, Z., & Jafari, M., 2020. Investigation on dynamical structure and moisture sources of heavy precipitation in south and south-west of Iran. Arabian Journal of Geosciences, 13(21), 1-15. - Lee, S., & Vink, K. (2015). Assessing the vulnerability of different age groups regarding flood fatalities: Case study in the Philippines. Journal of Water Policy, 17(6), 1045–1061. - Liu, D., Fan, Z., Fu, Q., Li, M., Faiz, M. A., Ali, S., … & Khan, M. I. (2020). Random forest regression evaluation model of regional flood disaster resilience based on the whale optimization algorithm. Journal of Cleaner Production, 250, 119468. - Liu, Y., Yuan, X., Guo, L., Huang, Y., & Zhang, X. (2017). Driving force analysis of the temporal and spatial distribution of flash floods in Sichuan Province. Journal of Sustainability, 9(9), 1527. - Machado, R. A. S., Oliveira, A. G., & Lois-González, R. C. (2019). Urban ecological infrastructure: The importance of vegetation cover in the control of floods and landslides in Salvador/Bahia, Brazil. Journal of Land Use Policy, 89, 104180. - Mallakpour, I., Villarini, G., Jones, M. P., & Smith, J. A. (2017). On the use of Cox regression to examine the temporal clustering of flooding and heavy precipitation across the central United States. Journal of Global and Planetary Change, 155, 98-108. - Miceli, R., Sotgiu, I., & Settanni, M. (2008). Disaster preparedness and perception of flood risk: A study in an alpine valley in Italy. Journal of Environmental Psychology, 28(2), 164–173. - Norouzi, G., & Taslimi, M. (2012). The impact of flood damages on production of Iran’s agricultural sector. Middle East Journal of Scientific Research, 12, 921-926. - Ogato, G. S., Bantider, A., Abebe, K., & Geneletti, D. (2020). Geographic information system (GIS)-Based multicriteria analysis of flooding hazard and risk in Ambo Town and its watershed, west Shoa zone, Oromia regional state, Ethiopia. Journal of Hydrology: Regional Studies, 27, 100659. - Sadeghi-Pouya, A., Nouri, J., Mansouri, N., & Kia-Lashaki, A. (2017). An indexing approach to assess flood vulnerability in the western coastal cities of Mazandaran, Iran. International Journal of Disaster Risk Reduction, 22, 304-316. - Saksena, S., & Merwade, V. (2015). Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. Journal of Hydrology, 530, 180-194. - Salvati, P., Petrucci, O., Rossi, M., Bianchi, C., Pasqua, A. A., & Guzzetti, F. (2018). Gender, age and circumstances analysis of flood and landslide fatalities in Italy. Journal of Science of the Total Environment, 610, 867–879. - Seckin, N., & Guven, A. (2012). Estimation of peak flood discharges at ungauged sites across Turkey. Journal of Water Resources Management, 26(9), 2569-2581. - Shabanikiya, H., Seyedin, H., Haghani, H., & Ebrahimian, A. (2014). Behavior of crossing flood on foot, associated risk factors and estimating a predictive model. Journal of Natural Hazards, 73(2), 1119–1126. - Shahabi, H., Shirzadi, A., Ronoud, S., Asadi, S., Pham, B. T., Mansouripour, F., … & Bui, D. T. (2021). Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Journal of Geoscience Frontiers, 12(3), 101100. - Smith, K. (2013). Environmental hazards: Assessing risk and reducing disaster. Routledge. - Tang, J., Li, Y., Cui, S., Xu, L., Hu, Y., Ding, S., & Nitivattananon, V. (2021). Analyzing the spatiotemporal dynamics of flood risk and its driving factors in a coastal watershed of southeastern China. Journal of Ecological Indicators, 121, 107134. - Turgut, A., & Tevfik, T. (2012). Floods and drowning incidents by floods. Journal of World Applied Sciences, 16(8), 1158–1162. - Wang, C., Du, S., Wen, J., Zhang, M., Gu, H., Shi, Y., & Xu, H. (2017). Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression. Journal of Stochastic Environmental Research Risk Assessment, 31(7), 1777-1790. - Yari, A., Ardalan, A., Ostadtaghizadeh, A., Zarezadeh, Y., Boubakran, M. S., Bidarpoor, F., & Rahimiforoushani, A. (2019). Underlying factors affecting death due to flood in Iran: A qualitative content analysis. International Journal of Disaster Risk Reduction, 40, 101258. - Zorn, C. R., & Shamseldin, A. Y. (2015). Peak flood estimation using gene expression programming. Journal of Hydrology, 531, 1122-1128. Abstract The purpose of the current study is to analyze the frequency of peak flood discharge with a 100-year return period in 206 Iran watersheds and to quantify it based on the most important factors. In this regard, flood frequency analysis was performed based on annual maximum discharge data and fitting of conventional continuous distributions in hydrology and fitting statistical tests. Then, for modeling, 8 parameters affecting the flood peak discharge including heavy daily rainfall, average vegetation, area, perimeter, average slope, average elevation, length of the main river, and the slope of the main river at the catchment area leading to the extraction of selected hydrometric stations. Also, the stepwise regression analysis technique was used to determine the factors affecting the production of flood peak discharge in the selected stations. The results of the study showed that the southwestern, southern, and southeastern basins of Iran with peak discharges of more than 4000 m3/s had the highest 100-year peak discharges among the study basins. The results of the stepwise regression model indicated that five parameters of area, heavy rainfall, elevation, vegetation, and slope of the basin with an adjusted coefficient of determination of 0.72, standard error of estimation of 132.7, Akaike's information criterion of 1.62, and variance inflation factor of 0.62 had the best performance in estimating the flood peak discharge. The results of this study, considering its large spatial scale, which includes the whole of Iran, can be used as a practical guide by the hydrologists and decision-makers in estimating the 100-year flood peak discharge in ungauged watersheds based on the most important factors affecting its generations. Introduction Flood is one of the most important natural hazards that has attracted a lot of attention from managers and planners due to the heavy damage it has caused to human societies (Jahangir et al., 2019). In fact, floods, as a type of natural disaster, have a significant negative impact on regional development, and its catastrophes are characterized by sudden water flow, high intensity, uncontrollable factors, and serious damages (Miceli et al., 2008). On the other hand, among various types of natural disasters such as earthquakes, landslides, soil erosion, and tsunamis, floods are considered to be the most common and destructive phenomena of the earth that affect the lives of many people every year (Doocy et al., 2013; Salvati et al., 2018; Yari et al., 2019). High socio-economic losses, human casualties, widespread destruction, and threatening living conditions are some of the damages that floods can cause (Turgut & Tevfik, 2012). It can be stated that half of the deaths occur due to floods (FitzGerald et al., 2010; Lee & Vink, 2015). In recent years, Iran has experienced very destructive floods due to climate change and poor watershed management (deforestation, overgrazing, and lack of flood control measures). For example, the recent floods (2019) in Iran have affected 25 provinces, killed 77 people, and caused about $ 2.2 billion in damage to these 25 provinces (Khosravi et al., 2020). Methodology In the first step, the Iran hydrometric stations that had discharge data with maximum long-term annual peak records (at least 30 years) were collected from the Iran Water Resources Management Company. In the next step, flood frequency analysis was performed based on the fitting of conventional continuous distributions in hydrology and fitting statistical tests. After performing flood frequency analysis and estimating peak discharge for 100 year return period, the watersheds boundary of hydrometric stations was determined. In this regard, using a digital elevation model with 12.5 m resolution and ARC GIS, Global Mapper, and Surfer software, the boundaries of the studied watersheds were extracted. Then, using the watersheds boundary and digital elevation model, the geomorphic parameters of the watershed such as perimeter, area, average slope, average elevation, length of the main river, and the slope of the main river were calculated. In the next step, long-term daily precipitation data of synoptic stations were collected from Iran Meteorological Organization. Then, 95% of the non-zero daily precipitation series was calculated for heavy precipitation (Gu et al., 2017). Using the IDW method, the long-term amount of heavy rainfall for each watershed was determined in GIS software. The NDVI index was used to determine the mean annual vegetation. In this regard, the vegetation time series for each watershed was extracted using Landsat images from 2000 to 2019 with a resolution of 30 m on the Google Earth Engine platform. After calculating the 100-year return period and possible parameters influencing the flood in the study watersheds, using Pearson bivariate analysis and stepwise regression model, the most suitable models for estimating flood peak discharge were determined. Discussion The results of the study show that the southwestern, southern, and southeastern watersheds of Iran with peak discharges of more than 4000 cubic meters per second have the highest peak discharges of 100 years among the study watersheds. Meanwhile, the Minab watershed, which ends in the Persian Gulf, has a maximum peak flow of 100 years with a peak flow of 12,614 cubic meters per second. On the other hand, the northwestern and northern watersheds of Iran with a peak discharge of less than 300 cubic meters per second have the lowest peak discharge, with a minimum discharge of 20.7 cubic meters per second related to the Solan watershed in Hamadan province. The findings of the stepwise regression model indicated that the five parameters of the watershed, including area, heavy rainfall, mean elevation, vegetation, and mean slope with R2 = 0.72 and significance level of 0.01, are the most influential factors in the estimation of flood peak discharge. In addition, the results showed that the three factors of watershed area, heavy rainfall, and mean slope have a direct relationship with peak discharge but mean elevation and vegetation have an inverse relationship. Conclusion This study quantified the relative contribution of driving factors influencing the flood peak discharge over 100 years across Iran. Considering its large spatial scale, which includes the whole of Iran, it can be used as a practical guide by the hydrologists and decision-makers in estimating the 100-year flood peak discharge in ungauged watersheds based on the most important factors affecting its generations. Keywords: Flood Peak Discharge, Modeling, Iran’s Watersheds, Stepwise Regression, Geomorphic Factors. References - Adhikari, P., Hong, Y., Douglas, K. R., Kirschbaum, D. B., Gourley, J., Adler, R., & Brakenridge, G. R. (2010). A digitized global flood inventory (1998–2008): Compilation and preliminary results. Journal of Natural Hazards, 55(2), 405–422. - Ahern, M., Kovats, R. S., Wilkinson, P., Few, R., & Matthies, F. (2005). Global health impacts of floods: Epidemiologic evidence. Journal of Epidemiologic Reviews, 27(1), 36–46. - Bennett, B., Leonard, M., Deng, Y., & Westra, S. (2018). An empirical investigation into the effect of antecedent precipitation on flood volume. Journal of Hydrology, 567, 435–445. - Chang, H. S., & Chen, T. L. (2016). Spatial heterogeneity of local flood vulnerability indicators within flood-prone areas in Taiwan. Journal of Environmental Earth Sciences, 75(23), 1-14. - Chau, K. W. (2017). Use of meta-heuristic techniques in rainfall-runoff modelling. Basel, Switzerland: Multidisciplinary Digital Publishing Institute. - Chuntian, C., & Chau, K. W. (2002). Three-person multi-objective conflict decision in reservoir flood control. European Journal of Operational Research, 142(3), 625–631. - Croke, J., Thompson, C., & Fryirs, K. (2017). Prioritising the placement of riparian vegetation to reduce flood risk and end-of-catchment sediment yields: Important considerations in hydrologically-variable regions. Journal of Environmental Management, 190, 9–19. - Doocy, S., Daniels, A., Packer, C., Dick, A., & Kirsch, T. D. (2013). The human impact of earthquakes: A historical review of events 1980-2009 and systematic literature review. Journal of Plos Currents, 5. - El-Hames, A. S. (2012). An empirical method for peak discharge prediction in ungauged arid and semi-arid region catchments based on morphological parameters and SCS curve number. Journal of Hydrology, 456, 94-100. - Ezemonye, M. N., & Emeribe, C. N. (2011). Flood characteristics and management adaptations in parts of the Imo river system. Ethiopian Journal of Environmental Studies and Management, 4(3), 56–64. - Falah, F., Rahmati, O., Rostami, M., Ahmadisharaf, E., Daliakopoulos, I. N., & Pourghasemi, H. R. (2019). Artificial neural networks for flood susceptibility mapping in data-scarce urban areas. In Spatial modeling in GIS and R for Earth and Environmental Sciences (pp. 323-336). Elsevier. - Feng, L. H., & Lu, J. (2010). The practical research on flood forecasting based on artificial neural networks. Journal of Expert Systems with Applications, 37(4), 2974–2977. - FitzGerald, G., Du, W., Jamal, A., Clark, M., & Hou, X. (2010). Flood fatalities in contemporary Australia (1997–2008). Journal of Emergency Medicine Australasia, 22(2), 180–186. - Ghavidel, Y., & Hombari, F. J. (2020). Synoptic analysis of unexampled super-heavy rainfall on April 1, 2019, in west of Iran. Journal of Natural Hazards, 104(2), 1567-1580. - Gu, X., Zhang, Q., Singh, V. P., & Shi, P. (2017). Changes in magnitude and frequency of heavy precipitation across China and its potential links to summer temperature. Journal of Hydrology, 547, 718–731. - Haghizadeh, A., Siahkamari, S., Haghiabi, A. H., & Rahmati, O. (2017). Forecasting flood-prone areas using Shannon’s entropy model. Journal of Earth System Science, 126(3), 1-11. - Haynes, K., Coates, L., van den Honert, R., Gissing, A., Bird, D., de Oliveira, F. D., … & Radford, D. (2017). Exploring the circumstances surrounding flood fatalities in Australia, 1900–2015 and the implications for policy and practice. Journal of Environmental Science & Policy, 76, 165–176. - Herget, J., Roggenkamp, T., & Krell, M. (2014). Estimation of peak discharges of historical floods. Journal of Hydrology and Earth System Sciences, 18(10), 4029. - Jahangir, M. H., Reineh, S. M. M., & Abolghasemi, M. (2019). Spatial predication of flood zonation mapping in Kan River Watershed, Iran, using artificial neural network algorithm. Journal of Weather and Climate Extremes, 25, 100215. - Khosravi, K., Panahi, M., Golkarian, A., Keesstra, S. D., Saco, P. M., Bui, D. T., & Lee, S. (2020). Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. Journal of Hydrology, 591, 125552. - Khosravi, K., Pourghasemi, H. R., Chapi, K., & Bahri, M. (2016). Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: A comparison between Shannon’s entropy, statistical index, and weighting factor models. Journal of Environmental Monitoring and Assessment, 188(12), 1-21. - Kirch, W., Menne, B., & Bertollini, R. (Eds.). (2005). Extreme weather events and public health responses. Berlin, Heidelberg: Springer Berlin Heidelberg. - Lashkari, H., Mohammadi, Z., & Jafari, M., 2020. Investigation on dynamical structure and moisture sources of heavy precipitation in south and south-west of Iran. Arabian Journal of Geosciences, 13(21), 1-15. - Lee, S., & Vink, K. (2015). Assessing the vulnerability of different age groups regarding flood fatalities: Case study in the Philippines. Journal of Water Policy, 17(6), 1045–1061. - Liu, D., Fan, Z., Fu, Q., Li, M., Faiz, M. A., Ali, S., … & Khan, M. I. (2020). Random forest regression evaluation model of regional flood disaster resilience based on the whale optimization algorithm. Journal of Cleaner Production, 250, 119468. - Liu, Y., Yuan, X., Guo, L., Huang, Y., & Zhang, X. (2017). Driving force analysis of the temporal and spatial distribution of flash floods in Sichuan Province. Journal of Sustainability, 9(9), 1527. - Machado, R. A. S., Oliveira, A. G., & Lois-González, R. C. (2019). Urban ecological infrastructure: The importance of vegetation cover in the control of floods and landslides in Salvador/Bahia, Brazil. Journal of Land Use Policy, 89, 104180. - Mallakpour, I., Villarini, G., Jones, M. P., & Smith, J. A. (2017). On the use of Cox regression to examine the temporal clustering of flooding and heavy precipitation across the central United States. Journal of Global and Planetary Change, 155, 98-108. - Miceli, R., Sotgiu, I., & Settanni, M. (2008). Disaster preparedness and perception of flood risk: A study in an alpine valley in Italy. Journal of Environmental Psychology, 28(2), 164–173. - Norouzi, G., & Taslimi, M. (2012). The impact of flood damages on production of Iran’s agricultural sector. Middle East Journal of Scientific Research, 12, 921-926. - Ogato, G. S., Bantider, A., Abebe, K., & Geneletti, D. (2020). Geographic information system (GIS)-Based multicriteria analysis of flooding hazard and risk in Ambo Town and its watershed, west Shoa zone, Oromia regional state, Ethiopia. Journal of Hydrology: Regional Studies, 27, 100659. - Sadeghi-Pouya, A., Nouri, J., Mansouri, N., & Kia-Lashaki, A. (2017). An indexing approach to assess flood vulnerability in the western coastal cities of Mazandaran, Iran. International Journal of Disaster Risk Reduction, 22, 304-316. - Saksena, S., & Merwade, V. (2015). Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. Journal of Hydrology, 530, 180-194. - Salvati, P., Petrucci, O., Rossi, M., Bianchi, C., Pasqua, A. A., & Guzzetti, F. (2018). Gender, age and circumstances analysis of flood and landslide fatalities in Italy. Journal of Science of the Total Environment, 610, 867–879. - Seckin, N., & Guven, A. (2012). Estimation of peak flood discharges at ungauged sites across Turkey. Journal of Water Resources Management, 26(9), 2569-2581. - Shabanikiya, H., Seyedin, H., Haghani, H., & Ebrahimian, A. (2014). Behavior of crossing flood on foot, associated risk factors and estimating a predictive model. Journal of Natural Hazards, 73(2), 1119–1126. - Shahabi, H., Shirzadi, A., Ronoud, S., Asadi, S., Pham, B. T., Mansouripour, F., … & Bui, D. T. (2021). Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Journal of Geoscience Frontiers, 12(3), 101100. - Smith, K. (2013). Environmental hazards: Assessing risk and reducing disaster. Routledge. - Tang, J., Li, Y., Cui, S., Xu, L., Hu, Y., Ding, S., & Nitivattananon, V. (2021). Analyzing the spatiotemporal dynamics of flood risk and its driving factors in a coastal watershed of southeastern China. Journal of Ecological Indicators, 121, 107134. - Turgut, A., & Tevfik, T. (2012). Floods and drowning incidents by floods. Journal of World Applied Sciences, 16(8), 1158–1162. - Wang, C., Du, S., Wen, J., Zhang, M., Gu, H., Shi, Y., & Xu, H. (2017). Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression. Journal of Stochastic Environmental Research Risk Assessment, 31(7), 1777-1790. - Yari, A., Ardalan, A., Ostadtaghizadeh, A., Zarezadeh, Y., Boubakran, M. S., Bidarpoor, F., & Rahimiforoushani, A. (2019). Underlying factors affecting death due to flood in Iran: A qualitative content analysis. International Journal of Disaster Risk Reduction, 40, 101258. - Zorn, C. R., & Shamseldin, A. Y. (2015). Peak flood estimation using gene expression programming. Journal of Hydrology, 531, 1122-1128.

https://gep.ui.ac.ir/article_26367_c32a7c3ec1779816e57de07312c9a9b9.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 33 2 2022 06 22 Barriers and Facilitators of Agri-Tourism Sustainable Development in West of Mazandaran Province Barriers and Facilitators of Agri-Tourism Sustainable Development in West of Mazandaran Province 39 66 26059 10.22108/gep.2021.130270.1454 FA Reza Solymannejad PhD Student of Rural Development, Agricultural Extension and Education Department, College of Agriculture & Natural Resources, Razi University, Kermanshah, Iran. Amirhossein Alibaygi Associate Professor, Agricultural Extension and Education Department, College of Agriculture & Natural Resources, Razi University, Kermanshah, Iran Laleh Salehi Assistant Professor, Agricultural Extension and Education Department, College of Agriculture & Natural Resources, Razi University, Kermanshah, Iran. Journal Article 2021 08 31 Abstract Agritourism can promote socio-cultural values in rural areas by creating new job opportunities, helping to preserve the environment, and reversing rural-urban migration flows. However, studies have shown that this type of tourism in Iran has not yet become acceptable for development. The aim of this research was to identify the barriers to and facilitators of sustainable development of agro-tourism in the west of Mazandaran Province based on an exploratory sequential mixed (qualitative-quantitative) method. First, 21 experts in the field of agritourism were purposefully selected from the quality departments of various organizations through semi-structured interviews. After collecting the necessary information, 120 indicators were extracted by using Conventional Content Analysis (CCA), which were then categorized in 8 socio-cultural, physical-physical, political-legal, management, intellectual infrastructure, communication-advertising, participatory, and marketing dimensions. The statistical population in the quantitative section included 69 rural residents in the western villages of Mazandaran Province, who were selected via a multi-stage method. To analyze the data, force field analysis was done by using SPSSwin20 and Pathmakerver5.5 software. The results showed that the outcomes of the deterrent forces of all the 8 factors were higher than those of the promoters, which revealed infeasibility of the sustainable development of agritourism. For each factor, the strongest facilitator and obstacle were identified and introduced. In conclusion, it should be said that it is necessary to facilitate agritourism development by strengthening the driving forces and removing obstacles to reach the equilibrium point and ultimately sustainability. Extended abstract Introduction: Agritourism is an activity to attract tourists to an area in order to diversify agricultural activities and receive tourists on farms. Most countries of the world have considered this type of tourism as a new strategy for socio-economic development, revitalization, and reconstruction of rural areas. Agricultural tourism is a value-added product that introduces additional revenues from production lands and farm brands to customers. This is an opportunity to create a loyal consumer basis for all agricultural products. This type of tourism can provide farmers with economic incentives so as to maintain their agricultural lands and the related natural resources. Despite the advantages and benefits of agritourism, it can cause environmental degradation, illegal construction, destruction of wildlife, dispersal of plant species, spread of wastes, and loss of local culture quality in the absence of proper educational promotion among farmers, residents, and tourists. Hence, in their tourism development studies, experts and thinkers from different countries have emphasized the importance and necessity of paying attention to sustainability in the tourism sector. Sustainable tourism development is a type of development, in which the balance of the values and quality of ethics with economic principles and advantages is maintained and efforts are made to replace the balanced development with a purely economical comprehensive development. Studies have shown that agritourism, especially in the west of Mazandaran Province, has not been very prosperous and there are no specific programs and actions for its development to be considered by the relevant organizations and officials. Therefore, this issue was addressed in the present study so as to identify the facilitators of and barriers to this type of sustainable development. Methodology: This research was conducted based on an exploratory sequential mixed (qualitative-quantitative) method. In the first stage, i.e., the qualitative part, the obstacles to and promoters of agricultural tourism development were identified through semi-structured interviews with 21 tourism experts from various organizations. Selection of the experts was done based on a purposeful method. The results of the qualitative part finally led to identifying 50 indicators and 70 deterrents, which were categorized in the 8 socio-cultural, physical-physical, political-legal, managerial, communication-advertising, intellectual infrastructure, participatory, and marketing factors based on their natures in order to conduct the quantitative part of the research by assessing each of the drivers and inhibitors of agritourism development in the form of a questionnaire. The questions were based on a 5-part Likert scale. In this part, a force field analysis was utilized. This method is a technique for identifying and analyzing the forces that affect a problem situation. The statistical population included a small part of rural villagers living in the west of Mazandaran Province. Due to the population dispersion and extent of distribution, a multi-stage method was applied for sampling, through which 69 general villagers were employed to complete the questionnaire. The obtained data were coded, analyzed, and described based on the force field analysis method using SPSSWIN20 and Pathmakerver5.5 software. Results: The following results were achieved for the current situation assessed by the driving and restraining forces related to the 8 mentioned factors affecting agritourism sustainability in the studied area: The highest scores of the physical-physical driving factors and barriers were related to the indices of the region's susceptibility to planning of and investment in agritourism and the tendencies of most people to use the private spaces of their villages in the forms of villas and private gardens, respectively. The highest scores of the political-legal driving factors and deterrents were related to the indices of providing appropriate measures to prevent the change of garden and agricultural land use to residential land use and difficulty in obtaining permits, respectively. The highest scores of management driving factors and deterrents were related to the indices of holding scientific educational workshops, personal meetings, and agricultural conferences and lack of concern for agricultural tourism among city officials, respectively. The highest scores of communication-advertising factors and barriers were related to the indices of timely information for holding agricultural exhibitions and weak advertisement for identifying agritourism capabilities in the region, respectively. The highest scores of the factors promoting and hindering the intellectual infrastructure were related to the indices of academic attention to agriculture and existence of a university unit in the province and lack of indigenous people’s education in the different surrounding regions, respectively. The highest scores of the participatory drivers and deterrents were related to the indices of villagers and indigenous peoples’ participations in the tourist guidance program and weak participation of travel agencies in agricultural tourism, respectively. Finally, the highest scores of marketing drivers and deterrents were related to the indices of tourist demand for agricultural tourism purposes and local organic products and lack of local people and tourists’ knowledge of the market and its capacities, respectively. The results of the obtained scores revealed that out of the 8 factors of promotion and deterrence in the mentioned dimensions, the scores of the deterrents were higher, indicating that the deterrents were stronger in the current situation. In general, agriculture in the current situation in western Mazandaran was associated with more obstacles and greater deterrents. Conclusion: The results demonstrated that the outcome scores of the deterrent forces were higher than those of the driving forces, and thus, the deterrent forces were stronger than the driving forces in the current situation. This indicated that sustainable development of agricultural tourism in the studied area was not possible based on the present driving factors. Therefore, according to the suggestions and solutions presented for each factor in the section of suggestions, it is necessary to strengthen the driving forces and weaken the inhibitors, the end-result of which will be agritourism sustainability. Keywords: sustainable development, agritourism, facilitator, force field analysis References: - Anderson, J. R. (2003). Risk management in rural development: A review of the Rural Development Family. The World Bank’s Rural Development Strategy, 20(3), 4-14. - Altinay, M. & Kashif, H. (2015). Sustainable tourism development: a case study of North. Journal of Annals of Tourism Research, 27(3), 37-52. - Arachi, D. J. (2017). Agri-tourism: Family Style. Cornhusker Economics, University of Nebraska–Lincoln Extension, 4(2), 1-25. - Baulcomb, J. S. (2003). Management of Change through Force Field Analysis. Journal of Nursing Management, 11(3), 80-275. - Bahatta, K., Itagaki, M., & Ohe, Y. (2019). Determinant Facyors of Farmer's Willingness to Start Agritourism in Rural Nepal. Journal of Agricultural Extension and Rural Development, 4(1), 146-167. - Bagi, F. S. & Reeder, R. J. (2012), Factors affecting farmer participation in agritourism. Agricultural and Resource Economics Review, 2(41), 189-199. - Ciolac, R., Adamov, T., Iancu, T., Popescu, G., Lile, R., Rujescu, C., & Marin, D. (2019). Agritourism: A sustainable Development Factor for Improving the Health of Rural Settlements (Case Study: Apuseni Mountains Area). International Journal of Business Tourism and Applied Sciences, 4(3), 1-24. - Clemens, R. (2014). Keeping Farmers on the Land: Agritourism in the European Union. Iowa Ag Review, 10(3), 8-9. - Calina, A., Calina, J., & Iancu. T. (2017). Research regarding the implementation, development, and impact of Agritourism on Pomania's Rural areas between 1990 and 2015. Environ. Eng. Manage, 16(4), 157-168. - Flanigan, S., Blackstock, K., & Hunter, C. (2015). Generating public and private benefits through understanding what drives different types of agritourism. Journal of Rural Studies, 41: 129-141. - Gao, J., Barbieri, C., & Valdivia, C. (2013). Agricultural landscape preferences: Implications for agritourism development. Journal of Travel Research, 3(53), 366-379. - Giaccio, V., Mastronardi. L., Marino, D., Giannelli. A., & Scardera. A. (2018). Do Rural Policies Impact on Tourism Development in Italy? A Case Study of Agritourism Employment and Income Growth from Sustainable Tourism. 10(8). 29-38. - Icoz, O., Pırnar, I., & Gunlu, E. (2010). The Agri-tourism potential of the Aegean region: SWOT analysis and suggestions for improvement. Passion for Hospitality Excellence, 14(3), 25-38. - Kumar, S. (2001). Force field analysis: applications in PRA, PLA notes.London. IIED, 199(36), 17-23. - Kisi, N. A. (2019). Strategic approach to sustainable tourism development using the a'wot hybrid method: A case study of Zonguldak, Turkey. Sustainability, 11(2), 964-983. - Lucha, C., Ferreira, G., Walker, M. A., & Groover, G. E. (2014). A Geographic Analysis of Agritourism in Virginia. Virginia Cooperative Extension, (62), 1-17. - Mahmoodi, M., Chizari, M., Kalantari, K., & Eftekhari, A. R. (2014). The Quantitative Strategic Planning Matrix (QSPM) applied to agri-tourism: A case study in coastal provinces of Iran. International Journal of Business Tourism and Applied Sciences, 2(2), 74-82. - Mohd Said, H., Chui Yee, C., & Mei Fung, O. )2012(. A SWOT analysis on agrotourism destination: A case on rural development in a small town Sekinchan Selangor, Malaysia. International Journal of Business and Management Studies, 1(2), 29-43. - Mak, A. H. N. and Chang, R. (2019). The driving and restraining forces for environmental strategy adoption in the hotel industry: A force field analysis approach. Tourism Management, 73, 48-60. - Parkar, P. (2015). Developing agritourism in Ratnagiri District of Konkan (Maharashtra): issues and challenges. Online International Interdisciplinary Research Journal, 5(2), 145-152. - Rietbergen-McCracken, J. and Narayan, D. (1998). Participation and social assessment: tools and techniques. The International Bank for Reconstruction and Development (IBRD), World Bank Research Institute: N.W., Washington D. C., USA. - Su, B. (2011). Rural Tourism in China. Tourism Management, 32(6), 38-41. - Yang, L. I. (2012). Impacts and Challenges in Agritourism Development in Yunnan, China. Tourism Planning & Development. 9(4), 81-369. Abstract Agritourism can promote socio-cultural values in rural areas by creating new job opportunities, helping to preserve the environment, and reversing rural-urban migration flows. However, studies have shown that this type of tourism in Iran has not yet become acceptable for development. The aim of this research was to identify the barriers to and facilitators of sustainable development of agro-tourism in the west of Mazandaran Province based on an exploratory sequential mixed (qualitative-quantitative) method. First, 21 experts in the field of agritourism were purposefully selected from the quality departments of various organizations through semi-structured interviews. After collecting the necessary information, 120 indicators were extracted by using Conventional Content Analysis (CCA), which were then categorized in 8 socio-cultural, physical-physical, political-legal, management, intellectual infrastructure, communication-advertising, participatory, and marketing dimensions. The statistical population in the quantitative section included 69 rural residents in the western villages of Mazandaran Province, who were selected via a multi-stage method. To analyze the data, force field analysis was done by using SPSSwin20 and Pathmakerver5.5 software. The results showed that the outcomes of the deterrent forces of all the 8 factors were higher than those of the promoters, which revealed infeasibility of the sustainable development of agritourism. For each factor, the strongest facilitator and obstacle were identified and introduced. In conclusion, it should be said that it is necessary to facilitate agritourism development by strengthening the driving forces and removing obstacles to reach the equilibrium point and ultimately sustainability. Extended abstract Introduction: Agritourism is an activity to attract tourists to an area in order to diversify agricultural activities and receive tourists on farms. Most countries of the world have considered this type of tourism as a new strategy for socio-economic development, revitalization, and reconstruction of rural areas. Agricultural tourism is a value-added product that introduces additional revenues from production lands and farm brands to customers. This is an opportunity to create a loyal consumer basis for all agricultural products. This type of tourism can provide farmers with economic incentives so as to maintain their agricultural lands and the related natural resources. Despite the advantages and benefits of agritourism, it can cause environmental degradation, illegal construction, destruction of wildlife, dispersal of plant species, spread of wastes, and loss of local culture quality in the absence of proper educational promotion among farmers, residents, and tourists. Hence, in their tourism development studies, experts and thinkers from different countries have emphasized the importance and necessity of paying attention to sustainability in the tourism sector. Sustainable tourism development is a type of development, in which the balance of the values and quality of ethics with economic principles and advantages is maintained and efforts are made to replace the balanced development with a purely economical comprehensive development. Studies have shown that agritourism, especially in the west of Mazandaran Province, has not been very prosperous and there are no specific programs and actions for its development to be considered by the relevant organizations and officials. Therefore, this issue was addressed in the present study so as to identify the facilitators of and barriers to this type of sustainable development. Methodology: This research was conducted based on an exploratory sequential mixed (qualitative-quantitative) method. In the first stage, i.e., the qualitative part, the obstacles to and promoters of agricultural tourism development were identified through semi-structured interviews with 21 tourism experts from various organizations. Selection of the experts was done based on a purposeful method. The results of the qualitative part finally led to identifying 50 indicators and 70 deterrents, which were categorized in the 8 socio-cultural, physical-physical, political-legal, managerial, communication-advertising, intellectual infrastructure, participatory, and marketing factors based on their natures in order to conduct the quantitative part of the research by assessing each of the drivers and inhibitors of agritourism development in the form of a questionnaire. The questions were based on a 5-part Likert scale. In this part, a force field analysis was utilized. This method is a technique for identifying and analyzing the forces that affect a problem situation. The statistical population included a small part of rural villagers living in the west of Mazandaran Province. Due to the population dispersion and extent of distribution, a multi-stage method was applied for sampling, through which 69 general villagers were employed to complete the questionnaire. The obtained data were coded, analyzed, and described based on the force field analysis method using SPSSWIN20 and Pathmakerver5.5 software. Results: The following results were achieved for the current situation assessed by the driving and restraining forces related to the 8 mentioned factors affecting agritourism sustainability in the studied area: The highest scores of the physical-physical driving factors and barriers were related to the indices of the region's susceptibility to planning of and investment in agritourism and the tendencies of most people to use the private spaces of their villages in the forms of villas and private gardens, respectively. The highest scores of the political-legal driving factors and deterrents were related to the indices of providing appropriate measures to prevent the change of garden and agricultural land use to residential land use and difficulty in obtaining permits, respectively. The highest scores of management driving factors and deterrents were related to the indices of holding scientific educational workshops, personal meetings, and agricultural conferences and lack of concern for agricultural tourism among city officials, respectively. The highest scores of communication-advertising factors and barriers were related to the indices of timely information for holding agricultural exhibitions and weak advertisement for identifying agritourism capabilities in the region, respectively. The highest scores of the factors promoting and hindering the intellectual infrastructure were related to the indices of academic attention to agriculture and existence of a university unit in the province and lack of indigenous people’s education in the different surrounding regions, respectively. The highest scores of the participatory drivers and deterrents were related to the indices of villagers and indigenous peoples’ participations in the tourist guidance program and weak participation of travel agencies in agricultural tourism, respectively. Finally, the highest scores of marketing drivers and deterrents were related to the indices of tourist demand for agricultural tourism purposes and local organic products and lack of local people and tourists’ knowledge of the market and its capacities, respectively. The results of the obtained scores revealed that out of the 8 factors of promotion and deterrence in the mentioned dimensions, the scores of the deterrents were higher, indicating that the deterrents were stronger in the current situation. In general, agriculture in the current situation in western Mazandaran was associated with more obstacles and greater deterrents. Conclusion: The results demonstrated that the outcome scores of the deterrent forces were higher than those of the driving forces, and thus, the deterrent forces were stronger than the driving forces in the current situation. This indicated that sustainable development of agricultural tourism in the studied area was not possible based on the present driving factors. Therefore, according to the suggestions and solutions presented for each factor in the section of suggestions, it is necessary to strengthen the driving forces and weaken the inhibitors, the end-result of which will be agritourism sustainability. Keywords: sustainable development, agritourism, facilitator, force field analysis References: - Anderson, J. R. (2003). Risk management in rural development: A review of the Rural Development Family. The World Bank’s Rural Development Strategy, 20(3), 4-14. - Altinay, M. & Kashif, H. (2015). Sustainable tourism development: a case study of North. Journal of Annals of Tourism Research, 27(3), 37-52. - Arachi, D. J. (2017). Agri-tourism: Family Style. Cornhusker Economics, University of Nebraska–Lincoln Extension, 4(2), 1-25. - Baulcomb, J. S. (2003). Management of Change through Force Field Analysis. Journal of Nursing Management, 11(3), 80-275. - Bahatta, K., Itagaki, M., & Ohe, Y. (2019). Determinant Facyors of Farmer's Willingness to Start Agritourism in Rural Nepal. Journal of Agricultural Extension and Rural Development, 4(1), 146-167. - Bagi, F. S. & Reeder, R. J. (2012), Factors affecting farmer participation in agritourism. Agricultural and Resource Economics Review, 2(41), 189-199. - Ciolac, R., Adamov, T., Iancu, T., Popescu, G., Lile, R., Rujescu, C., & Marin, D. (2019). Agritourism: A sustainable Development Factor for Improving the Health of Rural Settlements (Case Study: Apuseni Mountains Area). International Journal of Business Tourism and Applied Sciences, 4(3), 1-24. - Clemens, R. (2014). Keeping Farmers on the Land: Agritourism in the European Union. Iowa Ag Review, 10(3), 8-9. - Calina, A., Calina, J., & Iancu. T. (2017). Research regarding the implementation, development, and impact of Agritourism on Pomania's Rural areas between 1990 and 2015. Environ. Eng. Manage, 16(4), 157-168. - Flanigan, S., Blackstock, K., & Hunter, C. (2015). Generating public and private benefits through understanding what drives different types of agritourism. Journal of Rural Studies, 41: 129-141. - Gao, J., Barbieri, C., & Valdivia, C. (2013). Agricultural landscape preferences: Implications for agritourism development. Journal of Travel Research, 3(53), 366-379. - Giaccio, V., Mastronardi. L., Marino, D., Giannelli. A., & Scardera. A. (2018). Do Rural Policies Impact on Tourism Development in Italy? A Case Study of Agritourism Employment and Income Growth from Sustainable Tourism. 10(8). 29-38. - Icoz, O., Pırnar, I., & Gunlu, E. (2010). The Agri-tourism potential of the Aegean region: SWOT analysis and suggestions for improvement. Passion for Hospitality Excellence, 14(3), 25-38. - Kumar, S. (2001). Force field analysis: applications in PRA, PLA notes.London. IIED, 199(36), 17-23. - Kisi, N. A. (2019). Strategic approach to sustainable tourism development using the a'wot hybrid method: A case study of Zonguldak, Turkey. Sustainability, 11(2), 964-983. - Lucha, C., Ferreira, G., Walker, M. A., & Groover, G. E. (2014). A Geographic Analysis of Agritourism in Virginia. Virginia Cooperative Extension, (62), 1-17. - Mahmoodi, M., Chizari, M., Kalantari, K., & Eftekhari, A. R. (2014). The Quantitative Strategic Planning Matrix (QSPM) applied to agri-tourism: A case study in coastal provinces of Iran. International Journal of Business Tourism and Applied Sciences, 2(2), 74-82. - Mohd Said, H., Chui Yee, C., & Mei Fung, O. )2012(. A SWOT analysis on agrotourism destination: A case on rural development in a small town Sekinchan Selangor, Malaysia. International Journal of Business and Management Studies, 1(2), 29-43. - Mak, A. H. N. and Chang, R. (2019). The driving and restraining forces for environmental strategy adoption in the hotel industry: A force field analysis approach. Tourism Management, 73, 48-60. - Parkar, P. (2015). Developing agritourism in Ratnagiri District of Konkan (Maharashtra): issues and challenges. Online International Interdisciplinary Research Journal, 5(2), 145-152. - Rietbergen-McCracken, J. and Narayan, D. (1998). Participation and social assessment: tools and techniques. The International Bank for Reconstruction and Development (IBRD), World Bank Research Institute: N.W., Washington D. C., USA. - Su, B. (2011). Rural Tourism in China. Tourism Management, 32(6), 38-41. - Yang, L. I. (2012). Impacts and Challenges in Agritourism Development in Yunnan, China. Tourism Planning & Development. 9(4), 81-369.

https://gep.ui.ac.ir/article_26059_494d2822b35b90d62630f7f9e33c37fa.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 33 2 2022 06 22 Evaluation and Prediction of Land-Use Changes using the CA_Markov Model Evaluation and Prediction of Land-Use Changes using the CA_Markov Model 67 84 26294 10.22108/gep.2022.130601.1458 FA Zeynab Karimzadeh Motlagh PhD Candidate of Environment Sciences, Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran. Ali Lotfi Assistant Professor of Environmental Sciences, Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran. Saeid Pourmanafi 3- Assistant Professor of Environmental Sciences, Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran. Saeedreza Ahmadizadeh 4- Associate Professor of Environmental Sciences, Department of Natural Resources, Birjand University, Birjand, Iran Journal Article 2021 09 15 Abstract The purpose of this study was to model and predict temporal and spatial patterns of land-use change in the Zayandehrud basin. In this research, the CA-Markov prediction model was used to simulate and predict land-use change. First, the land-use changes from 1996 to 2018 were studied and then the future changes for 2030 and 2050 were simulated. Afterward, the future land-use scenarios were designed. The model was validated by comparing the simulated map of 2018 with the real map, and the kappa coefficient of 94 % was utilized to evaluate the model. Based on the results, the Built-up land-use was altered from 13016 hectares in 1996 to 154194 hectares in 2050. This outcome necessitates the management of the future development of the city. Furthermore, the amount of agricultural land was varied from 177067 hectares in 1996 to 40,000 hectares in 2050. Among all the changes, agricultural lands attracted the most attention and concerns. The results indicated the land-use changes in the form of urban areas and reducing area of agricultural lands. Such alterations were taken place in two distinct stages: urban lands have been developing since 2013, with a direct impact on the reduction of vegetation due to the conversion of agricultural lands into other land-uses. The dynamic trend of changes has also been confirmed and intensified since 1996. In 2018, a significant area of agricultural lands was converted into urban and industrial areas. In addition, the agricultural and orchard lands were 74057 hectares in 2018 and can be reduced to 40,000 hectares by 2050. It revealed 34057 hectares lost as compared to the agricultural and orchard lands in 2018. The present study depicts that the expansion of urban and industrial activities and reducing the level of agricultural land in the region requires more attention and care in land management. Extended Abstract: Introduction: Land use/Land cover (LULC) change is one of the main issues of sustainable development. To provide a rational science for regional planning decisions and sustainable development, land use pattern prediction models based on past preliminary information can be used to construct future scenarios of land-use changes. Modeling and predicting land use changes provide an interesting perspective for applications in planning units such as river basins and make it an effective tool for analyzing the causal dynamics of the future landscape under different scenarios. Land-use models are considered a powerful tool for understanding the spatio-temporal pattern of land-use changes, such as the Markov chain, cellular automation, and hybrid models based on these methods, which are widely used to simulate the spatial and temporal dimensions of land use. In the present study, land use changes prediction was performed using a combined model of cellular automation and the Markov chain (CA-Markov) to simulate temporal and spatial land-use patterns. The present study tries to predict land-use changes in the Zayandehrood river basin. The Zayandehrud basin is currently facing major environmental problems (such as water resources scarcity, population growth, urban development, and agricultural land degradation). Therefore, it is essential to evaluate land-use changes for this sensitive basin. In particular, the objectives of the research include two stages: 1) patial modeling of land-use change, and 2) predicting spatio-temporal patterns of land-use changes in the Zayandehrud river basin. Therefore, in the present research, land-use changes from 1996 to 2018 were investigated and future changes for 2030 and 2050 were simulated. Methodology: In this research, land-use changes modeling was performed in three time periods 1996 to 2013 (17-year period), 2013 to 2018 (5-year period), and 2018 to 2030 and 2050. The purpose of modeling is to determine the capabilities of the Markov chain model and integrate it with cellular automation to detect land-use changes. The images were classified into 4 classes: agriculture and gardens, built-up (urban areas, airport, and road), industrial towns, and other land uses (abandoned lands and fallow, rangeland, water areas). Finally, land-use changes modeling was performed in the period 1996 to 2050 (54-year period). The steps of the research method are as follows: Step 1: Pre-processing of satellite images: Radiometric correction was applied to the images. Next, the images were processed using the FLAASH module in ENVI5.3 software to reduce atmospheric interference. Then, by synthesizing the name and wavelength of the bands, image storage, mosaic, and mask clipping, a preprocessed remote sensing image was generated. Finally, the preprocessed remote sensing image was obtained. Step 2: Processing satellite images: Types of land use images in the area in ENVI 5.3 were extracted using visual interpretation and supervised classification methods. Land use classification algorithms were used to estimate the three main land-use classes (agriculture, urban, and industrial development). The principal component analysis method was performed on the images and was identified agricultural by high resolution. Land use classification for 1996, 2013, and 2018 was done with a classification approach based on the decision tree. To classify the images, maximum likelihood methods, artificial neural networks, and support vector machines were used. The final classification was performed using decision tree analysis. Finally, prediction of land-use changes was performed on images by performing the CA_Markov analysis in TerrSet software. Step 3: Post-processing of satellite images: Using Google Earth and cross-tab analysis, TerrSet software evaluated the accuracy of classifying land-use thematic maps. Using the existing database, a validation process was performed to ensure the accuracy of the model in predicting land-use changes for the forecasted 2018 map. The accuracy of the simulated model of land-use change in 2018 was validated and then compared with the actual map of the same year. The validation process was performed by generating the kappa coefficient. Discussion: In this study, land-use changes in the Zayandehrood basin were identified and investigated. The results showed that land-use changes are in the form of urban development and reduction of agricultural land use. Such changes have occurred in two distinct stages. First, urban land expansion has prevailed since 2013, with a direct impact on declining vegetation as a result of the conversion of agricultural land to other land uses. The dynamic trend of changes has also been confirmed and intensified since 1996. Because in 2018, a significant area of agricultural lands was converted into urban and industrial areas. Future scenarios based on the CA-Markov model provide valuable information about future land use and land cover changes in the study area. This study can identify land-use changes in different periods and depict the increase or decrease of important land uses in the region. According to the study of Motlagh et al. (2020), land-use changes were studied based on three possible scenarios (i.e. the current trend of land use growth, conservation of agricultural lands, and urban development forecast). Future scenarios for 2030 and 2050 estimate that there will be a significant reduction in vegetation and agricultural lands and orchards and continued urban and industrial development in areas along the Zayandehrood basin. Expansion of the agricultural sector along with the conservation of natural resources is not only one of the most important challenges of sustainable development in the Zayandehrud basin but is also essential for future strategic land use plans. Compilation of instructions for sustainable agricultural development can be a way to strike a balance between nature conservation and economic development in the region. Conclusion: In summary, this study demonstrates how the proposed CA-Markov model is used to better simulate land use complex and dynamic changes over time. Of all the land-use changes, the most worrying is the situation in the region for agricultural lands. If the current trend of land use continues, we estimate that by 2050, its area will be halved, and such changes in the landscape will undoubtedly change the entire ecosystem of the basin, emphasizing that the negative effects on the vegetation of the basin have a direct impact on the economic sector of the region because maintaining the quality of the environment of the Zayandehrood river basin is essential for ecotourism. Therefore, the management and planning of the basin are highly recommended to preserve its unique ecosystem, as well as to protect the vegetation in the area. The methods and results of this study will be useful for policymakers and urban planners for precise planning of the region to be able to manage the city using farms and conserving water resources and urban infrastructure development planning for environmentally sustainable development. Keywords: Land-Use Changes, Cellular Automation, the Markov Chain, Zayandehrud River Basin. References - Asgarian, A., Soffianian, A., Pourmanafi, S., & Bagheri, M. (2018). Evaluating the spatial effectiveness of alternative urban growth scenarios inprotecting cropland resources: A case of mixed agricultural-urbanized landscape in central Iran. Journal of Sustainable Cities and Society, 43, 197-207. - Assaf, C., Adamsa, C., Ferreira, F. F., & Françac, H. (2021). Land use and cover modeling as a tool for analyzing nature conservation policies – A case study of Juréia-Itatins. Journal of Land Use Policy, 100, 104895. - Aung, T. S., Fischer, T. B., & Buchanan, J. (2020). Land use and land cover changes along the China-Myanmar oil and gas pipelines-Monitoring infrastructure development in remote conflict-prone regions. PloS one, 15(8), e0237806. - Baqa, M. F., Chen, F., Lu, L., Qureshi, S., Tariq, A., Wang, S., … & Li, Q. (2021). Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-Markov model: A case study of Karachi, Pakistan. Land, 10(7), 700. - Cunha, E. R. D., Santos, C. A. G., da Silva, R. M., Bacani, V. M., & Pott, A. (2021). Future scenarios based on a CA-Markov land use and land cover simulation model for a tropical humid basin in the Cerrado/Atlantic forest ecotone of Brazil. Journal of Land Use Policy, 101, 105141. - Dey, N. N., Al Rakib, A., Kafy, A. A., & Raikwar, V. (2021). Geospatial modelling of changes in land use/land cover dynamics using Multi-layer perception Markov chain model in Rajshahi City, Bangladesh. Journal of Environmental Challenges, 4, 100148. - Gharaibeh, A., Shaamala, A., Obeidat, R., & Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon, 6(9), e05092. - Ghosh, S., Chatterjee, N. D., & Dinda, S. (2021). Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: A case study on Kolkata Metropolitan Area, India. Journal of Sustainable Cities and Society, 68, 102773. - Huang, Y., Yang, B., Wang, M., Liu, B., & Yang, X. (2020). Analysis of the future land cover change in Beijing using CA–Markov chain model. Journal of Environmental Earth Sciences, 79(2), 1-12. - Ji, G., Lai, Z., Xia, H., Liu, H., & Wang, Z. (2021). Future runoff variation and flood disaster prediction of the yellow river basin based on CA-Markov and SWAT. Land, 10(4), 421. - Khwarahm, N. R., Qader, S., Ararat, K., & Al-Quraishi, A. M. F. (2021). Predicting and mapping land cover/land use changes in Erbil /Iraq using CA-Markov synergy model. Journal of Earth Science Informatics, 14(1), 393–406. - Li, Q., Wang, L., Gul, H. N., & Li, D. (2021). Simulation and optimization of land use pattern to embed ecological suitability in an oasis region: A case study of Ganzhou district, Gansu province, China. Journal of Environmental Management, 287, 112321. - Matlhodi, B., Kenabatho, P. K., Parida, B. P., & Maphanyane, J. G. (2021). Analysis of the future land use land cover changes in the gaborone dam catchment using ca-markov model: Implications on water resources. Journal of Remote Sensing, 13(13), 2427. - Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Journal of Landscape and Urban Planning, 99(2), 141–153. - Motlagh, Z. K., Lotfi, A., Pourmanafi, S., Ahmadizadeh, S., & Soffianian, A. (2020). Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: Integration of remote sensing, CA Markov, and landscape metrics. Journal of Environmental Monitoring and Assessment, 192(11), 1-19. - Munthali, M. G., Mustak, S., Adeola, A., Botai, J., Singh, S. K., & Davis, N. (2020). Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model. Remote Sensing Applications: Society and Environment, 17, 100276. - Nath, B., Wang, Z., Ge, Y., Islam, K., Singh, R. P., & Niu, Z. (2020). Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process. International Journal of Geo-Information, 9(2), 134. - Rahnama, M. R. (2021). Forecasting land-use changes in Mashhad metropolitan area using cellular Automata and Markov chain model for 2016-2030. Journal of Sustainable Cities and Society, 64, 102548. - Ruben, G. B., Zhang, K., Dong, Z., & Xia, J. (2020). Analysis and projection of land-use/land-cover dynamics through scenario-based simulations using the CA-Markov model: A case study in guanting reservoir basin, China. Sustainability, 12(9), 3747. - Sibanda, S., & Ahmed, F. (2021). Modelling historic and future land use/land cover changes and their impact on wetland area in Shashe sub‑catchment, Zimbabwe. Journal of Modeling Earth Systems and Environment, 7(1), 57–70. - Silver, D., & Silva, T. H. (2021). A Markov model of urban evolution: Neighbourhood change as a complex process. Plos One, 16(1), e0245357. - Tang, F., Fu, M., Wang, L., Song, W., Yu, J., & Wu, Y. (2021). Dynamic evolution and scenario simulation of habitat quality under the impact of land-use change in the Huaihe river economic belt, China. Plos One, 16(4), e0249566. - Tariq, A., & Shu, H. (2020). CA-Markov chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of Faisalabad, Pakistan Aqil Tariq and Hong Shu. Remote Sensing, 12(20), 3402. - Tavangar, Sh., Moradi, H., Massah Bavani, A., & Gholamalifard, M. (2019). A futuristic survey of the effects of LU/LC change on stream flow by CA–Markov model: A case of the Nekarood watershed, Iran. Geocarto International, 36(10), 1100-1116. - Vinayak, B., Lee, H. S., & Gedem, S. (2021). Prediction of land use and land cover changes in Mumbai city, India, using remote sensing data and a multilayer perceptron neural network-based markov chain model. Sustainability, 13(2), 471. - Wang, Q., Guan, Q., Lin, J., Luo, H., Tan, Z., & Ma, Y. (2021). Simulating land use/land cover change in an arid region with the coupling models. Journal of Ecological Indicators, 122, 107231. - Wang, H., & Hu, Y. (2021). Simulation of biocapacity and spatial-temporal evolution analysis of Loess Plateau in northern shaanxi based on the CA–Markov model. Sustainability, 13(11), 5901. - Wang, S. W., Munkhnasan, L., & Lee, W. (2021). Land use and land cover change detection and prediction in Bhutan’s high-altitude city of Thimphu, using cellular automata and Markov chain. Journal of Environmental Challenges, 2, 100017. - Zhou, L., Dang, X., Sun, Q., & Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Journal of Sustainable Cities and Society, 55, 102045. Abstract The purpose of this study was to model and predict temporal and spatial patterns of land-use change in the Zayandehrud basin. In this research, the CA-Markov prediction model was used to simulate and predict land-use change. First, the land-use changes from 1996 to 2018 were studied and then the future changes for 2030 and 2050 were simulated. Afterward, the future land-use scenarios were designed. The model was validated by comparing the simulated map of 2018 with the real map, and the kappa coefficient of 94 % was utilized to evaluate the model. Based on the results, the Built-up land-use was altered from 13016 hectares in 1996 to 154194 hectares in 2050. This outcome necessitates the management of the future development of the city. Furthermore, the amount of agricultural land was varied from 177067 hectares in 1996 to 40,000 hectares in 2050. Among all the changes, agricultural lands attracted the most attention and concerns. The results indicated the land-use changes in the form of urban areas and reducing area of agricultural lands. Such alterations were taken place in two distinct stages: urban lands have been developing since 2013, with a direct impact on the reduction of vegetation due to the conversion of agricultural lands into other land-uses. The dynamic trend of changes has also been confirmed and intensified since 1996. In 2018, a significant area of agricultural lands was converted into urban and industrial areas. In addition, the agricultural and orchard lands were 74057 hectares in 2018 and can be reduced to 40,000 hectares by 2050. It revealed 34057 hectares lost as compared to the agricultural and orchard lands in 2018. The present study depicts that the expansion of urban and industrial activities and reducing the level of agricultural land in the region requires more attention and care in land management. Extended Abstract: Introduction: Land use/Land cover (LULC) change is one of the main issues of sustainable development. To provide a rational science for regional planning decisions and sustainable development, land use pattern prediction models based on past preliminary information can be used to construct future scenarios of land-use changes. Modeling and predicting land use changes provide an interesting perspective for applications in planning units such as river basins and make it an effective tool for analyzing the causal dynamics of the future landscape under different scenarios. Land-use models are considered a powerful tool for understanding the spatio-temporal pattern of land-use changes, such as the Markov chain, cellular automation, and hybrid models based on these methods, which are widely used to simulate the spatial and temporal dimensions of land use. In the present study, land use changes prediction was performed using a combined model of cellular automation and the Markov chain (CA-Markov) to simulate temporal and spatial land-use patterns. The present study tries to predict land-use changes in the Zayandehrood river basin. The Zayandehrud basin is currently facing major environmental problems (such as water resources scarcity, population growth, urban development, and agricultural land degradation). Therefore, it is essential to evaluate land-use changes for this sensitive basin. In particular, the objectives of the research include two stages: 1) patial modeling of land-use change, and 2) predicting spatio-temporal patterns of land-use changes in the Zayandehrud river basin. Therefore, in the present research, land-use changes from 1996 to 2018 were investigated and future changes for 2030 and 2050 were simulated. Methodology: In this research, land-use changes modeling was performed in three time periods 1996 to 2013 (17-year period), 2013 to 2018 (5-year period), and 2018 to 2030 and 2050. The purpose of modeling is to determine the capabilities of the Markov chain model and integrate it with cellular automation to detect land-use changes. The images were classified into 4 classes: agriculture and gardens, built-up (urban areas, airport, and road), industrial towns, and other land uses (abandoned lands and fallow, rangeland, water areas). Finally, land-use changes modeling was performed in the period 1996 to 2050 (54-year period). The steps of the research method are as follows: Step 1: Pre-processing of satellite images: Radiometric correction was applied to the images. Next, the images were processed using the FLAASH module in ENVI5.3 software to reduce atmospheric interference. Then, by synthesizing the name and wavelength of the bands, image storage, mosaic, and mask clipping, a preprocessed remote sensing image was generated. Finally, the preprocessed remote sensing image was obtained. Step 2: Processing satellite images: Types of land use images in the area in ENVI 5.3 were extracted using visual interpretation and supervised classification methods. Land use classification algorithms were used to estimate the three main land-use classes (agriculture, urban, and industrial development). The principal component analysis method was performed on the images and was identified agricultural by high resolution. Land use classification for 1996, 2013, and 2018 was done with a classification approach based on the decision tree. To classify the images, maximum likelihood methods, artificial neural networks, and support vector machines were used. The final classification was performed using decision tree analysis. Finally, prediction of land-use changes was performed on images by performing the CA_Markov analysis in TerrSet software. Step 3: Post-processing of satellite images: Using Google Earth and cross-tab analysis, TerrSet software evaluated the accuracy of classifying land-use thematic maps. Using the existing database, a validation process was performed to ensure the accuracy of the model in predicting land-use changes for the forecasted 2018 map. The accuracy of the simulated model of land-use change in 2018 was validated and then compared with the actual map of the same year. The validation process was performed by generating the kappa coefficient. Discussion: In this study, land-use changes in the Zayandehrood basin were identified and investigated. The results showed that land-use changes are in the form of urban development and reduction of agricultural land use. Such changes have occurred in two distinct stages. First, urban land expansion has prevailed since 2013, with a direct impact on declining vegetation as a result of the conversion of agricultural land to other land uses. The dynamic trend of changes has also been confirmed and intensified since 1996. Because in 2018, a significant area of agricultural lands was converted into urban and industrial areas. Future scenarios based on the CA-Markov model provide valuable information about future land use and land cover changes in the study area. This study can identify land-use changes in different periods and depict the increase or decrease of important land uses in the region. According to the study of Motlagh et al. (2020), land-use changes were studied based on three possible scenarios (i.e. the current trend of land use growth, conservation of agricultural lands, and urban development forecast). Future scenarios for 2030 and 2050 estimate that there will be a significant reduction in vegetation and agricultural lands and orchards and continued urban and industrial development in areas along the Zayandehrood basin. Expansion of the agricultural sector along with the conservation of natural resources is not only one of the most important challenges of sustainable development in the Zayandehrud basin but is also essential for future strategic land use plans. Compilation of instructions for sustainable agricultural development can be a way to strike a balance between nature conservation and economic development in the region. Conclusion: In summary, this study demonstrates how the proposed CA-Markov model is used to better simulate land use complex and dynamic changes over time. Of all the land-use changes, the most worrying is the situation in the region for agricultural lands. If the current trend of land use continues, we estimate that by 2050, its area will be halved, and such changes in the landscape will undoubtedly change the entire ecosystem of the basin, emphasizing that the negative effects on the vegetation of the basin have a direct impact on the economic sector of the region because maintaining the quality of the environment of the Zayandehrood river basin is essential for ecotourism. Therefore, the management and planning of the basin are highly recommended to preserve its unique ecosystem, as well as to protect the vegetation in the area. The methods and results of this study will be useful for policymakers and urban planners for precise planning of the region to be able to manage the city using farms and conserving water resources and urban infrastructure development planning for environmentally sustainable development. Keywords: Land-Use Changes, Cellular Automation, the Markov Chain, Zayandehrud River Basin. References - Asgarian, A., Soffianian, A., Pourmanafi, S., & Bagheri, M. (2018). Evaluating the spatial effectiveness of alternative urban growth scenarios inprotecting cropland resources: A case of mixed agricultural-urbanized landscape in central Iran. Journal of Sustainable Cities and Society, 43, 197-207. - Assaf, C., Adamsa, C., Ferreira, F. F., & Françac, H. (2021). Land use and cover modeling as a tool for analyzing nature conservation policies – A case study of Juréia-Itatins. Journal of Land Use Policy, 100, 104895. - Aung, T. S., Fischer, T. B., & Buchanan, J. (2020). Land use and land cover changes along the China-Myanmar oil and gas pipelines-Monitoring infrastructure development in remote conflict-prone regions. PloS one, 15(8), e0237806. - Baqa, M. F., Chen, F., Lu, L., Qureshi, S., Tariq, A., Wang, S., … & Li, Q. (2021). Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-Markov model: A case study of Karachi, Pakistan. Land, 10(7), 700. - Cunha, E. R. D., Santos, C. A. G., da Silva, R. M., Bacani, V. M., & Pott, A. (2021). Future scenarios based on a CA-Markov land use and land cover simulation model for a tropical humid basin in the Cerrado/Atlantic forest ecotone of Brazil. Journal of Land Use Policy, 101, 105141. - Dey, N. N., Al Rakib, A., Kafy, A. A., & Raikwar, V. (2021). Geospatial modelling of changes in land use/land cover dynamics using Multi-layer perception Markov chain model in Rajshahi City, Bangladesh. Journal of Environmental Challenges, 4, 100148. - Gharaibeh, A., Shaamala, A., Obeidat, R., & Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon, 6(9), e05092. - Ghosh, S., Chatterjee, N. D., & Dinda, S. (2021). Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: A case study on Kolkata Metropolitan Area, India. Journal of Sustainable Cities and Society, 68, 102773. - Huang, Y., Yang, B., Wang, M., Liu, B., & Yang, X. (2020). Analysis of the future land cover change in Beijing using CA–Markov chain model. Journal of Environmental Earth Sciences, 79(2), 1-12. - Ji, G., Lai, Z., Xia, H., Liu, H., & Wang, Z. (2021). Future runoff variation and flood disaster prediction of the yellow river basin based on CA-Markov and SWAT. Land, 10(4), 421. - Khwarahm, N. R., Qader, S., Ararat, K., & Al-Quraishi, A. M. F. (2021). Predicting and mapping land cover/land use changes in Erbil /Iraq using CA-Markov synergy model. Journal of Earth Science Informatics, 14(1), 393–406. - Li, Q., Wang, L., Gul, H. N., & Li, D. (2021). Simulation and optimization of land use pattern to embed ecological suitability in an oasis region: A case study of Ganzhou district, Gansu province, China. Journal of Environmental Management, 287, 112321. - Matlhodi, B., Kenabatho, P. K., Parida, B. P., & Maphanyane, J. G. (2021). Analysis of the future land use land cover changes in the gaborone dam catchment using ca-markov model: Implications on water resources. Journal of Remote Sensing, 13(13), 2427. - Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Journal of Landscape and Urban Planning, 99(2), 141–153. - Motlagh, Z. K., Lotfi, A., Pourmanafi, S., Ahmadizadeh, S., & Soffianian, A. (2020). Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: Integration of remote sensing, CA Markov, and landscape metrics. Journal of Environmental Monitoring and Assessment, 192(11), 1-19. - Munthali, M. G., Mustak, S., Adeola, A., Botai, J., Singh, S. K., & Davis, N. (2020). Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model. Remote Sensing Applications: Society and Environment, 17, 100276. - Nath, B., Wang, Z., Ge, Y., Islam, K., Singh, R. P., & Niu, Z. (2020). Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process. International Journal of Geo-Information, 9(2), 134. - Rahnama, M. R. (2021). Forecasting land-use changes in Mashhad metropolitan area using cellular Automata and Markov chain model for 2016-2030. Journal of Sustainable Cities and Society, 64, 102548. - Ruben, G. B., Zhang, K., Dong, Z., & Xia, J. (2020). Analysis and projection of land-use/land-cover dynamics through scenario-based simulations using the CA-Markov model: A case study in guanting reservoir basin, China. Sustainability, 12(9), 3747. - Sibanda, S., & Ahmed, F. (2021). Modelling historic and future land use/land cover changes and their impact on wetland area in Shashe sub‑catchment, Zimbabwe. Journal of Modeling Earth Systems and Environment, 7(1), 57–70. - Silver, D., & Silva, T. H. (2021). A Markov model of urban evolution: Neighbourhood change as a complex process. Plos One, 16(1), e0245357. - Tang, F., Fu, M., Wang, L., Song, W., Yu, J., & Wu, Y. (2021). Dynamic evolution and scenario simulation of habitat quality under the impact of land-use change in the Huaihe river economic belt, China. Plos One, 16(4), e0249566. - Tariq, A., & Shu, H. (2020). CA-Markov chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of Faisalabad, Pakistan Aqil Tariq and Hong Shu. Remote Sensing, 12(20), 3402. - Tavangar, Sh., Moradi, H., Massah Bavani, A., & Gholamalifard, M. (2019). A futuristic survey of the effects of LU/LC change on stream flow by CA–Markov model: A case of the Nekarood watershed, Iran. Geocarto International, 36(10), 1100-1116. - Vinayak, B., Lee, H. S., & Gedem, S. (2021). Prediction of land use and land cover changes in Mumbai city, India, using remote sensing data and a multilayer perceptron neural network-based markov chain model. Sustainability, 13(2), 471. - Wang, Q., Guan, Q., Lin, J., Luo, H., Tan, Z., & Ma, Y. (2021). Simulating land use/land cover change in an arid region with the coupling models. Journal of Ecological Indicators, 122, 107231. - Wang, H., & Hu, Y. (2021). Simulation of biocapacity and spatial-temporal evolution analysis of Loess Plateau in northern shaanxi based on the CA–Markov model. Sustainability, 13(11), 5901. - Wang, S. W., Munkhnasan, L., & Lee, W. (2021). Land use and land cover change detection and prediction in Bhutan’s high-altitude city of Thimphu, using cellular automata and Markov chain. Journal of Environmental Challenges, 2, 100017. - Zhou, L., Dang, X., Sun, Q., & Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Journal of Sustainable Cities and Society, 55, 102045.

https://gep.ui.ac.ir/article_26294_b85c2519b322f26b4344d80cba694b24.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 33 2 2022 06 22 Identifying and Prioritizing Cultural Barriers to Community-Based Tourism in Iran Identifying and Prioritizing Cultural Barriers to Community-Based Tourism in Iran 85 102 26348 10.22108/gep.2022.131406.1467 FA Azade Fallahi MA Student of Business Management, Department of Industrial Management, Faculty of Economics, Management, and Administrative Sciences, Semnan University, Semnan, Iran Meisam Modarresi Assistant Professor of Entrepreneurship Management, Department of Industrial Management, Faculty of Economics, Management, and Administrative Sciences, Semnan University, Semnan, Iran Azim Zarei Associate Professor of Business Management, Department of Industrial Management, Faculty of Economics, Management, and Administrative Sciences, Semnan University, Semnan, Iran Journal Article 2021 11 13 Abstract Community-based tourism is an approach to achieving sustainable tourism that focuses on community productivity. Being productive in the context of a society in the field of community-based tourism requires active participation, which is greatly influenced by the cultural atmosphere of the society. For this purpose, the present study has identified and prioritized the cultural challenges of tourism stakeholder participation in Iran. Initially, nine major challenges were extracted based on domestic and foreign literature and localized and prioritized using the SWARA method based on the integration of the opinions of seven tourism, urban, and academic affairs experts. Structural relationships between challenges were performed using interpretive structural modeling, in which challenges were balanced at five levels. The DEMATEL method was used to confirm the results. According to the findings of the present study, among the cultural challenges of community-based tourism, the inefficiency of mass media and lack of sense of belonging had the most impact and lack of trust and limited interactions, ambiguity, and indifference of individuals had the most interaction with other challenges, respectively. Introduction Achieving comprehensive and sustainable development is the main concern of most governments today. Community-based tourism (CBT) in its original form links community development strategies and sustainability in tourism activities. CBT is the interaction between different cultures in a single space and the benefit of all individuals from the benefits that are required, which requires principled management to take into account the preferences of all individuals. CBT must contribute to the independence of the society through comprehensive sustainable development. Local culture should be emphasized in the development of the community and at the grassroots level. The local cultural context should be considered and used as a starting point for community development projects. As tourism is a service and human-centered industry, the constructive role of the people for development should not be overlooked. People should be involved in the development of this industry and decisions and policies to participate in the implementation of programs and be well acquainted with the problems, obstacles, and benefits of this industry. Also because participation is greatly influenced by the cultural atmosphere of a community; therefore, the present study identifies and prioritizes cultural barriers affecting community-based tourism. Given the importance of community participation in the tourism industry, cultural barriers affecting the participation of tourism stakeholders have been identified. Using the interpretive structural modeling technique, a model of their internal relationships has been provided. This model is an appropriate approach in creating a hierarchical structure of factors, based on the degree of impact and effectiveness of each obstacle to form a comprehensive and clear view of key obstacles and how they relate to each other. Therefore, the main purpose of this study is to answer the following three research questions: 1) what are the cultural barriers affecting community-based tourism? 2) What are the priorities for the implementation of these criteria from the perspective of experts? 3) What are the causal relations of cultural barriers to community-based tourism? The study mentions the research method and findings, conclusions, and suggestions for future studies. The results of this study can be used by policymakers to introduce more systematic solutions to remove cultural barriers to community-based tourism and to help communities and entrepreneurs overcome the challenges of sustainable development. Materials and Methods The present study is applied and descriptive survey in terms of research orientation and data collection, respectively. At first, using domestic and foreign literature, nine of the most important cultural barriers of stakeholder participation in tourism were identified and then weighed by seven experts active in the field of tourism and urban management using the SWARA method. The feature of the researchers in the present study was to use the information of experts based on their deep knowledge of the relevant field and their relative knowledge of the sub-dimensions of the subject. After prioritizing the barriers, using the ISM method, a model of the internal relationships of the barriers with each other was prepared and DEMATEL software was used to confirm the model. Discussion of Results Prioritized barriers in the interpretive structural sector were aligned at five levels, with the polarization of the social environment at the first level and the inefficiency of the mass media at the fifth level. Findings in the DEMATEL section showed that barriers to mass media inefficiency, lack of sense of belonging, limited cultural capacity with the highest (D-R), respectively, were identified as the most influential factors, and barriers to distrust and limited interaction, ambiguity and apathy, and lack of honesty of members with the highest (D + R), respectively, had the most interaction. Conclusions In this study, by reviewing the research literature and using the opinions of experts, cultural barriers to community-based tourism were identified and prioritized. According to the findings of Dematel causal analysis, barriers to mass media inefficiency, lack of sense of belonging, limited cultural capacity, and differences in value and norm were identified as effective barriers. Due to the high impact of mass media on community participation, future studies could examine how the media works to evoke a sense of belonging in communities and public participation in tourism procurement, how media policies, by eliminating the polar atmosphere in communities, motivate participation in joint programs between the government and the nation, and basically how to create a culture of participation and innovation in the development of participatory tourism destinations. Keywords: Community-Based Tourism, Cultural Barriers to Participation, Interpretive Structural Modeling, SWARA, Iran. References - Adi, T. J. W., & Musbah, A. (2017). The Cultural Differences Influences on Knowledge Sharing Activities in Construction Project Collaboration. 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Community based tourism (CBT) planning– an analysis of opportunities and barriers: A case study of Cameroon. Unpublished Master Thesis, Eastern Mediterranean University, Gazimağusa, North Cyprus. - Le Bui Ngoc, A. (2019). The role of communication in leading a successful international project. Degree Programme in International Business. Saimaa University. - Lückmann, P., & Färber, K. (2016). The impact of cultural differences on project stakeholder engagement: A review of case study research in international project management. Journal of Procedia Computer Science, 100, 85-94. - Nazarian, A., Irani, Z., & Ali, M. (2013). The relationship between national culture and organisational culture: The case of Iranian private sector organisations. Journal of Economics, Business and Management, 1(1), 11-16. - Ochieng, E. G., & Price, A. D. (2009). Framework for managing multicultural project teams. Journal of Engineering, Construction and Architectural Management, 16(6), 527-543. - Offenbacker, B. S. (2004). Overcoming barriers to effective public participation. Journal of WIT Transactions on Ecology and the Environment, 70. - Razzaque, F. (2013). Role of mass media in facilitating citizen participation in Bangladesh public procurement. Journal of Cultural and Religious Studies, 5(12), 691-702. - Stanujkic, D., Karabasevic, D., & Zavadskas, E. K. (2015). A framework for the selection of a packaging design based on the SWARA method. Journal of Engineering Economics, 26(2), 181-187. - Stroope, S. (2011). How culture shapes community: Bible belief, theological unity, and a sense of belonging in religious congregations. The Sociological Quarterly Journal, 52(4), 568-592. - Tosun, C. (2000). Limits to community participation in the tourism development process in developing countries. Journal of Tourism Management, 21(6), 613-633. - Williams, L. (2004). Culture and community development: Towards new conceptualizations and pratice. Journal of Community Development, 39(4), 345-359. Abstract Community-based tourism is an approach to achieving sustainable tourism that focuses on community productivity. Being productive in the context of a society in the field of community-based tourism requires active participation, which is greatly influenced by the cultural atmosphere of the society. For this purpose, the present study has identified and prioritized the cultural challenges of tourism stakeholder participation in Iran. Initially, nine major challenges were extracted based on domestic and foreign literature and localized and prioritized using the SWARA method based on the integration of the opinions of seven tourism, urban, and academic affairs experts. Structural relationships between challenges were performed using interpretive structural modeling, in which challenges were balanced at five levels. The DEMATEL method was used to confirm the results. According to the findings of the present study, among the cultural challenges of community-based tourism, the inefficiency of mass media and lack of sense of belonging had the most impact and lack of trust and limited interactions, ambiguity, and indifference of individuals had the most interaction with other challenges, respectively. Introduction Achieving comprehensive and sustainable development is the main concern of most governments today. Community-based tourism (CBT) in its original form links community development strategies and sustainability in tourism activities. CBT is the interaction between different cultures in a single space and the benefit of all individuals from the benefits that are required, which requires principled management to take into account the preferences of all individuals. CBT must contribute to the independence of the society through comprehensive sustainable development. Local culture should be emphasized in the development of the community and at the grassroots level. The local cultural context should be considered and used as a starting point for community development projects. As tourism is a service and human-centered industry, the constructive role of the people for development should not be overlooked. People should be involved in the development of this industry and decisions and policies to participate in the implementation of programs and be well acquainted with the problems, obstacles, and benefits of this industry. Also because participation is greatly influenced by the cultural atmosphere of a community; therefore, the present study identifies and prioritizes cultural barriers affecting community-based tourism. Given the importance of community participation in the tourism industry, cultural barriers affecting the participation of tourism stakeholders have been identified. Using the interpretive structural modeling technique, a model of their internal relationships has been provided. This model is an appropriate approach in creating a hierarchical structure of factors, based on the degree of impact and effectiveness of each obstacle to form a comprehensive and clear view of key obstacles and how they relate to each other. Therefore, the main purpose of this study is to answer the following three research questions: 1) what are the cultural barriers affecting community-based tourism? 2) What are the priorities for the implementation of these criteria from the perspective of experts? 3) What are the causal relations of cultural barriers to community-based tourism? The study mentions the research method and findings, conclusions, and suggestions for future studies. The results of this study can be used by policymakers to introduce more systematic solutions to remove cultural barriers to community-based tourism and to help communities and entrepreneurs overcome the challenges of sustainable development. Materials and Methods The present study is applied and descriptive survey in terms of research orientation and data collection, respectively. At first, using domestic and foreign literature, nine of the most important cultural barriers of stakeholder participation in tourism were identified and then weighed by seven experts active in the field of tourism and urban management using the SWARA method. The feature of the researchers in the present study was to use the information of experts based on their deep knowledge of the relevant field and their relative knowledge of the sub-dimensions of the subject. After prioritizing the barriers, using the ISM method, a model of the internal relationships of the barriers with each other was prepared and DEMATEL software was used to confirm the model. Discussion of Results Prioritized barriers in the interpretive structural sector were aligned at five levels, with the polarization of the social environment at the first level and the inefficiency of the mass media at the fifth level. Findings in the DEMATEL section showed that barriers to mass media inefficiency, lack of sense of belonging, limited cultural capacity with the highest (D-R), respectively, were identified as the most influential factors, and barriers to distrust and limited interaction, ambiguity and apathy, and lack of honesty of members with the highest (D + R), respectively, had the most interaction. Conclusions In this study, by reviewing the research literature and using the opinions of experts, cultural barriers to community-based tourism were identified and prioritized. According to the findings of Dematel causal analysis, barriers to mass media inefficiency, lack of sense of belonging, limited cultural capacity, and differences in value and norm were identified as effective barriers. Due to the high impact of mass media on community participation, future studies could examine how the media works to evoke a sense of belonging in communities and public participation in tourism procurement, how media policies, by eliminating the polar atmosphere in communities, motivate participation in joint programs between the government and the nation, and basically how to create a culture of participation and innovation in the development of participatory tourism destinations. Keywords: Community-Based Tourism, Cultural Barriers to Participation, Interpretive Structural Modeling, SWARA, Iran. References - Adi, T. J. W., & Musbah, A. (2017). The Cultural Differences Influences on Knowledge Sharing Activities in Construction Project Collaboration. IPTEK Journal of Proceedings Series, 3(1), 85-89. - Agharafii, D. (2018). An analysis of mass media role in social changes of Tehran citizens. Journal of Organizational Behavior Research, 3(2), 1-11. - Alemanno, A. (2015). Stakeholder engagement in regulatory policy. Journal of Regulatory Policy Outlook, OECD Publishing, 1-62. Electronic copy available at: https://ssrn.com/abstract=2701675. - Alshboul, K. (2016). Assessing local community involvement in tourism development around a proposed world heritage site in Jerash. PhD Thesis, Waterloo University. - Anstrand, M. (2006). Community-based tourism and socio-culture aspects relating to tourism: A case study of a Swedish student excursion to Babati (Tanzania). (n.p). - Enserink, B., Patel, M., Kranz, N., & Maestu, J. (2007). Cultural factors as co-determinants of participation in river basin management. Journal of Ecology and Society, 12(2), 1-9. Available Online at: http://www.ecologyandsociety.org/ vol12/iss2/art24/ - Giampiccoli, A., & Hayward Kalis, J. (2012). Community-based tourism and local culture: The case of the amaMpondo, PASOS. Revista de Turismo y Patrimonio Cultural, 10(1), 173-188. - Helo, P., & Ajmal, M. M. (2015). Conceptualizing trust in global context with focus on international projects and operations. Proceedings of the University of Vaasa Reports 199. - Jhaiyanuntana, A., & Nomnian, S. (2020). Intercultural communication challenges and strategies for the Thai undergraduate hotel interns. PASAA: Journal of Language Teaching and Learning in Thailand, 59, 204-235. - Kia, A. A., Latifi, G., Rasooli, M., & Kazemnia, M. E. (2016). The role of the mass media in urban management (case study: 22-district of Tehran Municipality). Journal of Urban Economics and Management, 4(16), 115-126. - Kwangseh, B. E. (2014). Community based tourism (CBT) planning– an analysis of opportunities and barriers: A case study of Cameroon. Unpublished Master Thesis, Eastern Mediterranean University, Gazimağusa, North Cyprus. - Le Bui Ngoc, A. (2019). The role of communication in leading a successful international project. Degree Programme in International Business. Saimaa University. - Lückmann, P., & Färber, K. (2016). The impact of cultural differences on project stakeholder engagement: A review of case study research in international project management. Journal of Procedia Computer Science, 100, 85-94. - Nazarian, A., Irani, Z., & Ali, M. (2013). The relationship between national culture and organisational culture: The case of Iranian private sector organisations. Journal of Economics, Business and Management, 1(1), 11-16. - Ochieng, E. G., & Price, A. D. (2009). Framework for managing multicultural project teams. Journal of Engineering, Construction and Architectural Management, 16(6), 527-543. - Offenbacker, B. S. (2004). Overcoming barriers to effective public participation. Journal of WIT Transactions on Ecology and the Environment, 70. - Razzaque, F. (2013). Role of mass media in facilitating citizen participation in Bangladesh public procurement. Journal of Cultural and Religious Studies, 5(12), 691-702. - Stanujkic, D., Karabasevic, D., & Zavadskas, E. K. (2015). A framework for the selection of a packaging design based on the SWARA method. Journal of Engineering Economics, 26(2), 181-187. - Stroope, S. (2011). How culture shapes community: Bible belief, theological unity, and a sense of belonging in religious congregations. The Sociological Quarterly Journal, 52(4), 568-592. - Tosun, C. (2000). Limits to community participation in the tourism development process in developing countries. Journal of Tourism Management, 21(6), 613-633. - Williams, L. (2004). Culture and community development: Towards new conceptualizations and pratice. Journal of Community Development, 39(4), 345-359.

https://gep.ui.ac.ir/article_26348_3ff86d94d168a0a7e4e0656bd64cb4fe.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 33 2 2022 06 22 Evaluation of the Resilience of District 20 of Tehran Metropolitan Region (TMR) against Environmental Hazards Using Fuzzy Functions in GIS Software Evaluation of the Resilience of District 20 of Tehran Metropolitan Region (TMR) against Environmental Hazards Using Fuzzy Functions in GIS Software 103 130 26271 10.22108/gep.2022.130271.1453 FA Vafa Ghaem Maghami Ph.D. Student in Environmental Management, Department of Environmental Management Planning and Education, Faculty of Environment, University of Tehran, Tehran, Iran Ahmad Nohegar Professor of Geomorphology, Department of Management Planning and Environmental Education, Faculty of Environment, University of Tehran, Tehran, Iran Mohamad Javad Amiri Assistant Professor of Environment, Department of Environmental Management Planning and Education, Faculty of Environment, University of Tehran, Tehran, Iran Journal Article 2021 09 02 Extended abstract Introduction: The idea of resilience of different social, economic, physical, and managerial orientations has entered urban and regional studies on a large scale. This resilient system can absorb temporary or permanent crises and adapt to rapidly changing conditions without losing its function. Among these, resilience against natural disasters can be explained by how social, economic, institutional, political, and executive capacities of societies affect the increase of resilience and understanding of its dimensions in the society. Environmental crises, such as earthquakes, floods, fires, and climate pollution, have caused environmental vulnerability in cities and consequently created threats to their securities, especially in District 20 of Tehran City. By recognizing the dimensions of vulnerability in District 20 of this city against environmental crises, management strategies can be developed to reduce vulnerability and risks and enhance resilience. For this reason, the main purpose of this study was to evaluate resilience of the neighborhoods in District 20 of Tehran City against environmental crises. To achieve this goal, the Fuzzy Multi-Criteria Decision Model (FMCDM) and K-mean method of classification were used. Methodology: To identify and assess the resilience of District 20 of Tehran against environmental crises, a database was created based on the crises and its spatial information was prepared in 4 criteria and 26 sub-criteria. After creating the spatial database of the mentioned district and compiling the criteria and sub-criteria, a layer of information was prepared in ArcGIS software and a distance map was drawn for each sub-criterion through Euclidean distance mapping in order to measure and manage the resilience. Then, fuzzy operators were applied to draw each fuzzy map (subscale) with a value between 0 and 1. Analytic Network Process (ANP) method was utilized to weight and evaluate the research criteria and sub-criteria. Next, the map of each criterion and sub-criterion was drawn by combining the Euclidean distance and fuzzy operators multiplied by their fuzzy weights obtained from the ANP model in ArcGIS software. Thus, the final map was prepared for each criterion and sub-criterion, which showed their values of resilience to the environmental crises. Then, fuzzy superimposing operators were applied to superimpose the fuzzy weighting maps and a superimposed map of 26 sub-criteria (4 criteria) was obtained for each fuzzy operator. To identify the best fuzzy operator by superimposing the research sub-criteria, analysis of spatial relationships between the independent variables and the dependent variable was done through the Ordinary Least Squares (OLS) regression. Finally, the classical K-mean clustering method was employed to classify the neighborhoods from the perspective of resilience to environmental crises. Discussion: The results showed that the weights and values of the socio-economic criteria, road infrastructure, land use and accessibility in resilience measures were 0.49, 0.23, 0.16, and 0.11, respectively. In the socio-economic, road infrastructure, land use, and accessibility criteria, the sub-criteria of house strength, pedestrian bridge, access to social places, and access to medical centers with the weights of 0.33, 0.43, 0.32, and 0.29 had the highest values in resilience. Among the fuzzy superposition operators, the algebraic addition operator (SUM) had the highest correlation with the research criteria in identifying the resilience of the neighborhoods. The northeast and southeast neighborhoods, as well as the central neighborhoods of District 20 of Tehran, were the most resilient neighborhoods to environmental crises. In the final step of the current research, the classical K-mean method was used to cluster the existing neighborhoods in District 20 of Tehran City based on their resilience to environmental crises. The results revealed that the neighborhoods were divided into 3 clusters. In the first cluster showing a lot of patience, the neighborhoods of Javanmard Qassab, Mansouria and Mangal, Hamzehabad, Sartakht, Ibn Babavieh and Zahirabad, Taghiabad, and Abbasabad were located. In the second cluster indicating moderate tolerance, Dolatabad and Shahadat, Sadeghieh, Shahid Ghayuri, Deilman, Aqdasiyeh, Estakhr, and Alain neighborhoods were situated. Finally, the neighborhoods of Sizdeh Aban, Shahid Beheshti, Firoozabadi, Valiabad, and Hashemabad were located in the third cluster with poor productivity. Conclusion: Environmental crises, such as earthquake, flood, drought, air and water pollution, and fire, have the potential to become harmful in areas where there are no crisis management and risk mitigation. In the 21st century, the world has been hit by such environmental crises as Asian tsunamis, Hurricanes Katrina and Rita, successive earthquakes, flash floods, desert dust storms, and widespread fires. Although predictive tools are able to predict some disasters, future crises cannot be forecast based on empirical evidence. Therefore, increasing the ability of a system called resilience is very important for responding to such crises; yet, its resilience must first be measured. In the present study, the resilience of District 20 of Tehran City to environmental crises was evaluated based on socio-economic, road infrastructure, land use, and accessibility criteria. The results of this modeling led to the extraction of 3 clusters for the resilience of the neighborhoods of District 20 of Tehran against environmental crises. The neighborhoods in the west region had the highest resilience compared to the urban areas. Keywords: resilience, home strength, Analytic Network Process (ANP), fuzzy operator, regression References - Asadzadeh, A., Kötter, T., & Zebardast, E. (2015). An augmented approach for measurement of disaster resilience using connective factor analysis and analytic network process (F’ANP) model. International Journal of Disaster Risk Reduction, 14, 504-518. - Bacud, S. T. (2018). Integration of Indigenous and Scientific Knowledge in Disaster Risk Reduction: Resilience Building of a Marginalized Sampaguita Growing Community in the Philippines. Procedia engineering, 212, 511-518.‏ - Borsekova, K., Nijkamp, P., & Guevara, P. (2018). Urban resilience patterns after an external shock: An exploratory study. International journal of disaster risk reduction, 31, 381-392. - Caschili, S., Reggiani, A., & Medda, F. (2015). Resilience and vulnerability of spatial economic networks. Networks and Spatial Economics, 15(2), 205-210. - Chen, C., Xu, L., Zhao, D., Xu, T., & Lei, P. (2020). A new model for describing the urban resilience considering adaptability, resistance and recovery. Safety science, 128, 104756.‏ - Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J. (2008). A place-based model for understanding community resilience to natural disasters. Global environmental change, 18(4), 598-606.‏ - Davis, I., & Izadkhah, Y. O. (2006). Building resilient urban communities. Open House International, 31(1), 11-21. - Fakhruddin, B. S., Reinen-Hamill, R., & Robertson, R. (2019). Extent and evaluation of vulnerability for disaster risk reduction of urban Nuku'alofa, Tonga. Progress in Disaster Science, 2, 100017. - Govindarajulu, D. (2020). Strengthening institutional and financial mechanisms for building urban resilience in India. International Journal of Disaster Risk Reduction, 101549. - Harpin, S. B. (2019). Adverse childhood experiences and resilience: implications for marginalized and vulnerable young people. Journal of Adolescent Health, 64(1), 3-4.‏ - Kabir, M. H., Sato, M., Habbiba, U., & Yousuf, T. B. (2018). Assessment of Urban Disaster Resilience in Dhaka North City Corporation (DNCC), Bangladesh. Procedia engineering, 212, 1107-1114. - Landry, F., Dupras, J., & Messier, C. (2020). Convergence of urban forest and socio-economic indicators of resilience: A study of environmental inequality in four major cities in eastern Canada. Landscape and Urban Planning, 202, 103856.‏ - Moghadas, M., Asadzadeh, A., Vafeidis, A., Fekete, A., & Kötter, T. (2019). A multi-criteria approach for assessing urban flood resilience in Tehran, Iran. International journal of disaster risk reduction, 35, 101069. - Mullick, M. R. A., Tanim, A. H., & Islam, S. S. (2019). Coastal vulnerability analysis of Bangladesh coast using fuzzy logic based geospatial techniques. Ocean & Coastal Management, 174, 154-169.‏ - Ran, J., MacGillivray, B. H., Gong, Y., & Hales, T. C. (2019). The application of frameworks for measuring social vulnerability and resilience to geophysical hazards within developing countries: A systematic review and narrative synthesis. Science of the total environment, 134486. - Suárez, M., Gómez-Baggethun, E., Benayas, J., & Tilbury, D. (2016). Towards an urban resilience Index: a case study in 50 Spanish cities. Sustainability, 8(8), 774. - Wills, G., & Hofmeyr, H. (2019). Academic resilience in challenging contexts: Evidence from township and rural primary schools in South Africa. International Journal of Educational Research, 98, 192-205.‏ - Zhang, W., Su, S., Wang, B., Hong, Q., & Sun, L. (2020). Local k-NNs pattern in Omni-Direction graph convolution neural network for 3D point clouds. Neurocomputing, 413, 487-498.‏ - Zhang, X., Song, J., Peng, J., & Wu, J. (2019). Landslides-oriented urban disaster resilience assessment—a case study in ShenZhen, China. Science of the Total Environment, 661, 95-106. - Fig 1. Geographical location of District 20 Tehran - Table 1- Fuzzy membership of sub-criteria in resilience of District 20 of Tehran against environmental hazards - Fig 2. Diagram of the steps of the work method in the present study - Table 1- The weight of research criteria in resilience of District 20 of Tehran against environmental hazards - Tab 3- Weight of criteria socio-economic in the resilience of Tehran's 20th district Figure 3- Zoning of population and young population sub-criteria in the resilience of District 20 of Tehran - Figure 4- Zoning of Economic participation and employment rates sub-criteria in the resilience of District 20 of Tehran - Figure 5- Zoning of Home strength and literacy rates sub-criteria in the resilience of District 20 of Tehran - Tab 4- Weight of criteria and sub-criteria of land cover in the resilience of Tehran's 20th district - Figure 6- Zoning of Access to parks and social sites sub-criteria in the resilience of District 20 of Tehran - Figure 7- Zoning of Distance from the flood and access to water sources sub-criteria in the resilience of District 20 of Tehran - Figure 8- Zoning of Distance from agricultural lands and urban green space sub-criteria in the resilience of District 20 of Tehran - Figure 9- Zoning of Distance from the green belt and outdoor rates sub-criteria in the resilience of District 20 of Tehran - Tab 5- Standard weight and sub-criteria of accesses in Tehran 20 district resilience - Figure 10- Zoning of Access to fuel station and security police sub-criteria in the resilience of District 20 of Tehran - Figure 11- Zoning of Access to educational and administrative centers sub-criteria in the resilience of District 20 of Tehran - Figure 12- Zoning of Access to Commercial and service centers sub-criteria in the resilience of District 20 of Tehran - Figure 13- Zoning of Access to Medical centers and distance from the factory sub-criteria in the resilience of District 20 of Tehran - Tab 6- Standard weight and sub-criteria of road infrastructure in Tehran 20 district - Figure 14- Zoning of Access to bus and freeway stations sub-criteria in the resilience of District 20 of Tehran - Figure 15- Zoning of Access to the pedestrian bridge and railway station sub-criteria in the resilience of District 20 of Tehran - Figure 16- Zoning of Criteria for access to urban services and socio-economic criteria in the resilience of District 20 of Tehran - Figure 17- Zoning of Land use criteria and access to road infrastructure criteria in the resilience of District 20 of Tehran - Tab 7. Correlation coefficient between fuzzy overlay operators with research criteria - Fig 18. Overlapping of research criteria with SUM operator and resilience modeling of Tehran Region 20 Extended abstract Introduction: The idea of resilience of different social, economic, physical, and managerial orientations has entered urban and regional studies on a large scale. This resilient system can absorb temporary or permanent crises and adapt to rapidly changing conditions without losing its function. Among these, resilience against natural disasters can be explained by how social, economic, institutional, political, and executive capacities of societies affect the increase of resilience and understanding of its dimensions in the society. Environmental crises, such as earthquakes, floods, fires, and climate pollution, have caused environmental vulnerability in cities and consequently created threats to their securities, especially in District 20 of Tehran City. By recognizing the dimensions of vulnerability in District 20 of this city against environmental crises, management strategies can be developed to reduce vulnerability and risks and enhance resilience. For this reason, the main purpose of this study was to evaluate resilience of the neighborhoods in District 20 of Tehran City against environmental crises. To achieve this goal, the Fuzzy Multi-Criteria Decision Model (FMCDM) and K-mean method of classification were used. Methodology: To identify and assess the resilience of District 20 of Tehran against environmental crises, a database was created based on the crises and its spatial information was prepared in 4 criteria and 26 sub-criteria. After creating the spatial database of the mentioned district and compiling the criteria and sub-criteria, a layer of information was prepared in ArcGIS software and a distance map was drawn for each sub-criterion through Euclidean distance mapping in order to measure and manage the resilience. Then, fuzzy operators were applied to draw each fuzzy map (subscale) with a value between 0 and 1. Analytic Network Process (ANP) method was utilized to weight and evaluate the research criteria and sub-criteria. Next, the map of each criterion and sub-criterion was drawn by combining the Euclidean distance and fuzzy operators multiplied by their fuzzy weights obtained from the ANP model in ArcGIS software. Thus, the final map was prepared for each criterion and sub-criterion, which showed their values of resilience to the environmental crises. Then, fuzzy superimposing operators were applied to superimpose the fuzzy weighting maps and a superimposed map of 26 sub-criteria (4 criteria) was obtained for each fuzzy operator. To identify the best fuzzy operator by superimposing the research sub-criteria, analysis of spatial relationships between the independent variables and the dependent variable was done through the Ordinary Least Squares (OLS) regression. Finally, the classical K-mean clustering method was employed to classify the neighborhoods from the perspective of resilience to environmental crises. Discussion: The results showed that the weights and values of the socio-economic criteria, road infrastructure, land use and accessibility in resilience measures were 0.49, 0.23, 0.16, and 0.11, respectively. In the socio-economic, road infrastructure, land use, and accessibility criteria, the sub-criteria of house strength, pedestrian bridge, access to social places, and access to medical centers with the weights of 0.33, 0.43, 0.32, and 0.29 had the highest values in resilience. Among the fuzzy superposition operators, the algebraic addition operator (SUM) had the highest correlation with the research criteria in identifying the resilience of the neighborhoods. The northeast and southeast neighborhoods, as well as the central neighborhoods of District 20 of Tehran, were the most resilient neighborhoods to environmental crises. In the final step of the current research, the classical K-mean method was used to cluster the existing neighborhoods in District 20 of Tehran City based on their resilience to environmental crises. The results revealed that the neighborhoods were divided into 3 clusters. In the first cluster showing a lot of patience, the neighborhoods of Javanmard Qassab, Mansouria and Mangal, Hamzehabad, Sartakht, Ibn Babavieh and Zahirabad, Taghiabad, and Abbasabad were located. In the second cluster indicating moderate tolerance, Dolatabad and Shahadat, Sadeghieh, Shahid Ghayuri, Deilman, Aqdasiyeh, Estakhr, and Alain neighborhoods were situated. Finally, the neighborhoods of Sizdeh Aban, Shahid Beheshti, Firoozabadi, Valiabad, and Hashemabad were located in the third cluster with poor productivity. Conclusion: Environmental crises, such as earthquake, flood, drought, air and water pollution, and fire, have the potential to become harmful in areas where there are no crisis management and risk mitigation. In the 21st century, the world has been hit by such environmental crises as Asian tsunamis, Hurricanes Katrina and Rita, successive earthquakes, flash floods, desert dust storms, and widespread fires. Although predictive tools are able to predict some disasters, future crises cannot be forecast based on empirical evidence. Therefore, increasing the ability of a system called resilience is very important for responding to such crises; yet, its resilience must first be measured. In the present study, the resilience of District 20 of Tehran City to environmental crises was evaluated based on socio-economic, road infrastructure, land use, and accessibility criteria. The results of this modeling led to the extraction of 3 clusters for the resilience of the neighborhoods of District 20 of Tehran against environmental crises. The neighborhoods in the west region had the highest resilience compared to the urban areas. Keywords: resilience, home strength, Analytic Network Process (ANP), fuzzy operator, regression References - Asadzadeh, A., Kötter, T., & Zebardast, E. (2015). An augmented approach for measurement of disaster resilience using connective factor analysis and analytic network process (F’ANP) model. International Journal of Disaster Risk Reduction, 14, 504-518. - Bacud, S. T. (2018). Integration of Indigenous and Scientific Knowledge in Disaster Risk Reduction: Resilience Building of a Marginalized Sampaguita Growing Community in the Philippines. Procedia engineering, 212, 511-518.‏ - Borsekova, K., Nijkamp, P., & Guevara, P. (2018). Urban resilience patterns after an external shock: An exploratory study. International journal of disaster risk reduction, 31, 381-392. - Caschili, S., Reggiani, A., & Medda, F. (2015). Resilience and vulnerability of spatial economic networks. Networks and Spatial Economics, 15(2), 205-210. - Chen, C., Xu, L., Zhao, D., Xu, T., & Lei, P. (2020). A new model for describing the urban resilience considering adaptability, resistance and recovery. Safety science, 128, 104756.‏ - Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J. (2008). A place-based model for understanding community resilience to natural disasters. Global environmental change, 18(4), 598-606.‏ - Davis, I., & Izadkhah, Y. O. (2006). Building resilient urban communities. Open House International, 31(1), 11-21. - Fakhruddin, B. S., Reinen-Hamill, R., & Robertson, R. (2019). Extent and evaluation of vulnerability for disaster risk reduction of urban Nuku'alofa, Tonga. Progress in Disaster Science, 2, 100017. - Govindarajulu, D. (2020). Strengthening institutional and financial mechanisms for building urban resilience in India. International Journal of Disaster Risk Reduction, 101549. - Harpin, S. B. (2019). Adverse childhood experiences and resilience: implications for marginalized and vulnerable young people. Journal of Adolescent Health, 64(1), 3-4.‏ - Kabir, M. H., Sato, M., Habbiba, U., & Yousuf, T. B. (2018). Assessment of Urban Disaster Resilience in Dhaka North City Corporation (DNCC), Bangladesh. Procedia engineering, 212, 1107-1114. - Landry, F., Dupras, J., & Messier, C. (2020). Convergence of urban forest and socio-economic indicators of resilience: A study of environmental inequality in four major cities in eastern Canada. Landscape and Urban Planning, 202, 103856.‏ - Moghadas, M., Asadzadeh, A., Vafeidis, A., Fekete, A., & Kötter, T. (2019). A multi-criteria approach for assessing urban flood resilience in Tehran, Iran. International journal of disaster risk reduction, 35, 101069. - Mullick, M. R. A., Tanim, A. H., & Islam, S. S. (2019). Coastal vulnerability analysis of Bangladesh coast using fuzzy logic based geospatial techniques. Ocean & Coastal Management, 174, 154-169.‏ - Ran, J., MacGillivray, B. H., Gong, Y., & Hales, T. C. (2019). The application of frameworks for measuring social vulnerability and resilience to geophysical hazards within developing countries: A systematic review and narrative synthesis. Science of the total environment, 134486. - Suárez, M., Gómez-Baggethun, E., Benayas, J., & Tilbury, D. (2016). Towards an urban resilience Index: a case study in 50 Spanish cities. Sustainability, 8(8), 774. - Wills, G., & Hofmeyr, H. (2019). Academic resilience in challenging contexts: Evidence from township and rural primary schools in South Africa. International Journal of Educational Research, 98, 192-205.‏ - Zhang, W., Su, S., Wang, B., Hong, Q., & Sun, L. (2020). Local k-NNs pattern in Omni-Direction graph convolution neural network for 3D point clouds. Neurocomputing, 413, 487-498.‏ - Zhang, X., Song, J., Peng, J., & Wu, J. (2019). Landslides-oriented urban disaster resilience assessment—a case study in ShenZhen, China. Science of the Total Environment, 661, 95-106. - Fig 1. Geographical location of District 20 Tehran - Table 1- Fuzzy membership of sub-criteria in resilience of District 20 of Tehran against environmental hazards - Fig 2. Diagram of the steps of the work method in the present study - Table 1- The weight of research criteria in resilience of District 20 of Tehran against environmental hazards - Tab 3- Weight of criteria socio-economic in the resilience of Tehran's 20th district Figure 3- Zoning of population and young population sub-criteria in the resilience of District 20 of Tehran - Figure 4- Zoning of Economic participation and employment rates sub-criteria in the resilience of District 20 of Tehran - Figure 5- Zoning of Home strength and literacy rates sub-criteria in the resilience of District 20 of Tehran - Tab 4- Weight of criteria and sub-criteria of land cover in the resilience of Tehran's 20th district - Figure 6- Zoning of Access to parks and social sites sub-criteria in the resilience of District 20 of Tehran - Figure 7- Zoning of Distance from the flood and access to water sources sub-criteria in the resilience of District 20 of Tehran - Figure 8- Zoning of Distance from agricultural lands and urban green space sub-criteria in the resilience of District 20 of Tehran - Figure 9- Zoning of Distance from the green belt and outdoor rates sub-criteria in the resilience of District 20 of Tehran - Tab 5- Standard weight and sub-criteria of accesses in Tehran 20 district resilience - Figure 10- Zoning of Access to fuel station and security police sub-criteria in the resilience of District 20 of Tehran - Figure 11- Zoning of Access to educational and administrative centers sub-criteria in the resilience of District 20 of Tehran - Figure 12- Zoning of Access to Commercial and service centers sub-criteria in the resilience of District 20 of Tehran - Figure 13- Zoning of Access to Medical centers and distance from the factory sub-criteria in the resilience of District 20 of Tehran - Tab 6- Standard weight and sub-criteria of road infrastructure in Tehran 20 district - Figure 14- Zoning of Access to bus and freeway stations sub-criteria in the resilience of District 20 of Tehran - Figure 15- Zoning of Access to the pedestrian bridge and railway station sub-criteria in the resilience of District 20 of Tehran - Figure 16- Zoning of Criteria for access to urban services and socio-economic criteria in the resilience of District 20 of Tehran - Figure 17- Zoning of Land use criteria and access to road infrastructure criteria in the resilience of District 20 of Tehran - Tab 7. Correlation coefficient between fuzzy overlay operators with research criteria - Fig 18. Overlapping of research criteria with SUM operator and resilience modeling of Tehran Region 20

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University of Isfahan Geography and Environmental Planning 2008-5362 33 2 2022 06 22 Explaining the Model of the Creative City Approach in the Historical Context of Hamedan Explaining the Model of the Creative City Approach in the Historical Context of Hamedan 137 146 26560 10.22108/gep.2022.132572.1490 FA Razieh Mollamirzaei PhD Candidate, Department of Urban Planning, Faculty of Art and Architecture, Bu-Ali Sina University, Hamedan, Iran Hassan Sajadzadeh Associate Professor, Department of Urban Design, Faculty of Art and Architecture, Bu-Ali Sina University, Hamedan, Iran 0000-0002-3989-9389 Journal Article 2022 02 16 Abstract Creative city theory is a new approach whose milestone is the emphasis on creativity and culture in the economy and social capital. Historical contexts, in addition to having the aesthetic and identifying values of cities, are still the livelihood of millions of citizens and are therefore a good place to apply the creative city approach. The present study was conducted to explain the model of the creative city approach in the historical context of the Hamadan city, which has been done by the survey technique and the analytical-interpretive method. Initially, the evaluation indicators of the creative city, which were extracted from reliable sources, were classified into five structures: ‘socio-cultural’, ‘economic’, ‘managerial’, ‘functional-spatial’, and ‘environmental’. Then, the opinions of people and experts were collected through a questionnaire. Next, the data of the questionnaire were entered into SPSS software and with the help of the exploratory factor analysis technique which was applied separately in each of the above structures, the explanatory factors were identified. Then, using the linear multivariate regression analysis technique, the relationship between the extracted factors and the creative city approach in the historical context of Hamadan was assessed. Finally, the indicators of ‘women’s participation in social activities’, ‘the importance of knowledge-based service centers’, ‘historical events of the city’, ‘people's participation in social activities’, ‘the need to use new and knowledge-based technologies’, and ‘supporting urban entrepreneurs’ were identified. The elements have had the greatest impact on the realization of the creative city approach in the historical context of Hamedan. Introduction Historical contexts have valuable and unique features that distinguish them from other urban contexts. At the same time, this context is forced to accept changes to meet the needs of its residents, the speed of which must be commensurate with the needs of the citizens in that community. An environment that can not adapt to the needs of citizens has gradually entered a process that will lead to burnout. This confirms the need for planning and protection in historical contexts. During the industrialization of societies, the fascination with the endless use of new technology exposed many of the world’s historical contexts to serious threats and damage, some of them even perished during the effects of urban development. The exposure of today's cities to the unpredictable economic system has caused cities to rely more and more on their internal resources such as history, spaces, and creative force. The pressures of globalization and, consequently, the problems caused by the change in economic structure and the need to create a new identity have led cities to use their cultural assets to differentiate their identities and recreate the urban context (Richards & Palmer, 2010). The appropriate approach to the old and historical urban context requires a careful and comprehensive approach to the ancient context and its characteristics. Today, one of the techniques to achieve these goals is to emphasize the activities that are formed by combining different dimensions of economics and culture in a way of human logical order called creativity. Undoubtedly, historical contexts with rich cultural heritage resources are a good place to apply a creative approach. In recent decades, one of the new scientific and professional fields in the historical part of cities is the creative city approach. Methodology The present study has been done using the analytical-interpretive method based on documentary studies and field observations. In the present study, a combined method has been used, so the survey method was used as a quantitative method, and the focus group discussion method was used as a qualitative method. The conceptual framework was documented in a diagram. In the next step, extractive indices were collected in the study sample. Using factor analysis, the importance of the main factors affecting the creativity of the historical context of Hamadan was determined. Then, based on the extraction indices, a questionnaire was designed based on the Likert scale. The number of questionnaires in order to be valid for SPSS software analysis based on the Cochran sampling test was 200. The questionnaire was distributed electronically and people who have lived in the city of Hamedan and experts and professors in the field of urban planning, as well as city managers, have participated at this stage. By completing the questionnaires and entering them into SPSS software, the main factors of the creative city approach in the historical context of Hamadan city were extracted using the exploratory factor analysis method. To extract the priorities of the creative city, linear multivariate regression was performed between the extracted factors, and the coefficients of each factor and their importance were determined. Finally, the ‘creative city model in the historical context of Hamadan’ was obtained by obtaining the most important factors in this area. Discussion Factor analysis was performed using SPSS software after entering the questionnaire data in each of the 5 structures of the creative city, which were economic, environmental, institutional-governance, functional-spatial, and socio-cultural structures. According to the obtained model, the numerical value of all variables in the subscription table was more than 0.5, which indicated the appropriateness of the explanatory power of the model and the value of KMO statistics. The next output of the factor analysis was the KMO test. The KMO value is always between 0 and 1. If the value is less than 0.5, the data will not be suitable for factor analysis, and if the value is between 0.5 and 0.69, factor analysis should be done more carefully. But if this value is more than 0.7, the correlation between the data will be suitable for data analysis. On the other hand, the Bartlett test should be used to ensure that the data are suitable for factor analysis. Bartlett's test tests the hypothesis that the observed correlation matrix belongs to a society with unrelated variables. For this reason, before factor analysis, a correlation matrix between variables must be formed. If the correlation matrix is unit, it is unsuitable for factor analysis. The Bartlett test is significant when its probability is less than 0.05. After controlling and appropriateness of statistical tests that test and measure the raw data for use in factor analysis, the preliminary matrix is calculated in which the explained variance is determined by each factor. It should be noted that the specific values for all factors must be greater than 1. Then, after determining the variance of each of the factors explaining the main structures of the creative city, the factor matrix was rotated so that each of the relevant variables would have the most relationship with the factors and facilitates the conditions for naming and identifying the factors. After creating the rotated matrix of factors and using the position of variables in the structures of the creative city, the factors must be interpreted and named. After determining the main factors of the creative city approach in the historical context of Hamadan, it was necessary to understand the relationship between these factors and the realization of the creative city in the historical context. For this purpose, the linear relationship between the extracted factors and the creative city approach was investigated by the multiple linear regression method to determine the beta coefficient for the factors. Then, by multiplying the three values of ‘ load factor coefficient’, ‘factor-beta coefficient’, and ‘variable dissatisfaction rate’, the variables can be ranked as priorities for the realization of a creative city in the historical context of Hamadan. Conclusion Based on the exploratory factor analysis technique, indicators in 13 factors explaining the creative city approach including ‘historical-cultural heritage’, ‘social participation’, ‘creative class’, ‘social capital’, ‘socio-cultural diversity’, ‘creative governance’, ‘innovation’, ‘creative industries’, ‘environmental sustainability’, ‘green infrastructure’, ‘creative public space’, ‘functional diversity of space’, and ‘spatial identity structure’ were categorized. After that, the relationship between the extracted factors and the creative city approach in the historical context of Hamedan was measured by linear multivariate regression analysis technique, which revealed 4 factors, respectively, with the greatest impact: ‘historical-cultural heritage’, ‘creative class’, ‘innovation’, and ‘socio-cultural diversity’. Finally, by multiplying the three numerical values of ‘load factor coefficient’, ‘factor-beta coefficient’, and ‘average dissatisfaction’, the variables of the creative city approach were prioritized in the historical context of Hamedan, which showed the indicators of ‘women's participation in social activities in the historical context of the city’, ‘the importance of knowledge-based service centers’, ‘ historical events of the city’, ‘people's participation in social activities’, ‘the need to use new and knowledge-based technologies’, and ‘support for urban entrepreneurs’ were the most important indicators in achieving a creative city approach in the historical context of Hamedan. Citizens’ participation in urban affairs is a necessity today, which can lead to sustainable urban development. Creative urban management takes steps to solve urban problems by combining the ideas of modern urban managers with local values. Creative management strengthens the city and the socio-economic and cultural growth of citizens that people as human capital should be involved in the proper management of urban management. Achieving a creative city requires policies and programs that are coordinated and coherent, evolve, and require extensive collaboration between the public sector, at various levels of government, local and national, private sector actors, and all social institutions. For urban management to be creative, it must have a creative understanding of urban growth, urban life, and the need to engage in creative urban management while promoting its creative knowledge in the field of urban management. In such situations, it can creatively analyze urban issues and thus have a creative and innovative approach to finding ideas, and communicating with its citizens. Achieving these goals requires creating a culture and creating a suitable platform for citizenship education, the infrastructure of which is provided in the city and urban areas. In this regard, the presence of women with care and delicacy of their special look can accelerate this process. Keywords: Creative City, Historical Context, Model Explanation, Exploratory Factor Analysis, the Historical Context of Hamedan. References: - Anonymous (2010). Creative economy report 2010. The United Nations Conference on Trade and Development. - Bianchini, F., & Parkinson, M. (Eds.). (1993). Cultural policy and urban regeneration: The West European experience. Manchester: Manchester University Press. - Cohendet, P., Simon, L., Sole Parellada, F., & Valls Pasola, J. (2009). The creative city: a toolkit for urban innovators. Second Edition. London: Earthscan Publications Ltd. - Correia, C., & Oliveira, M. (2012). Creative indexes: Economic space matters?. MA Thesis in Economics. School of Economics and Business. University of Porto - Donegan, M., & Lowe, N. (2008). Inequality in the creative city: Is there still a place for ‘old-fashioned’ institutions?. Journal of Economic Development Quarterly, 22(1), 46–62. - d’Ovidio, M., & Cossu, A. (2017). Culture is reclaiming the creative city: The case of Macao in Milan, Italy. Journal of City, Culture and Society, 8, 7–12. - Ernawati, J. (2010). People ‘s impressions of a tourist-historic district. Indonesia: Brawijaya University Press. - European Commission. (2017). The cultural and creative cities monitor. Luxembourg: Publications Office of the European Union. - Evans, G. (2009). Creative cities, creative spaces and urban policy. Urban Studies, 46(5-6), 1003-1040. - Florida, R. (2002). The rise of the creative class: And how it's transforming work, leisure, community and everyday life. New York: Basic Books. - Florida, R. (2008). The rise of the creative class revisited. New York: Basic Books. - Florida, R. (2014). The creative class and economic development. Journal of Economic Development Quarterly, 28(3), 196-205. - Goldberg-Miller, S. B. (2019). Creative city strategies on the municipal agenda in New York. Journal of City, Culture and Society, 17, 26–37. - Hall, P. (2000). Creative cities and economic development. Journal of Urban Studies, 37(4), 639-649. - Kakiuchi, E. (2016). Culturally creative cities in Japan: Reality and prospects. Journal of City, Culture and Society, 7(2), 101-108. - Khoo, S. L. (2020). Towards an inclusive creative city: How ready is the historic city of George Town, Penang?. Journal of City, Culture and Society, 23, 100367. - Kotze, N., & De Vries, L. (2019). Resuscitating the African giant: Urban regeneration and inner-city redevelopment initiatives along the ‘Corridors of Freedom’ in downtown Johannesburg. Geographia Polonica, 92(1), 57–70. - Kuriakose, P. N., & Philip, S. (2021). City profile: Kochi, city-region - Planning measures to make Kochi smart and creative. Cities, 118, 103307. - Landry, C. (2010). Creativity, culture and the city: A question of inter connection. European Capital of Culture. - Montgomery, J. (2005). Beware ‘the creative class’. Creativity and wealth creation revisited. Local Economy: The Journal of the Local Economy Policy Unit, 20(4), 337–343. - Richards, G., & Palmer, R. (2010). Eventful cities: Cultural management and urban revitalisation. Routledge. - Rodrigues, M., & Franco, M. (2020). Networks and performance of creative cities: A bibliometric analysis. Journal of City, Culture and Society, 20, 100326. - Sasaki, M. (2008). Developing creative cities through networking. Journal of Policy Science, 15(3), 77-88. - Sasaki, M. (2010). Urban regeneration through cultural creativity and social inclusion: Rethinking creative city theory through a Japanese case study. Cities, 27, 3-9. - Scott, A. J. (2014). Beyond the creative city: Cognitive–cultural capitalism and the new urbanism. Journal of Regional Studies, 48(4), 565-578. - Vanolo, A. (2008). The image of the creative city: Some reflections on urban branding in Turin. Cities, 25(6), 370–382. - Vickery, J. (2011). Beyond the creative city-cultural policy in an age of scarcity. Made: A Centre for Place-Making, 1, 1-20. - Zimmerman, J. (2008). From brew, town to cool town: Neoliberalism and the creative city development strategy in Milwaukee. Cities, 25(4), 230–242. Abstract Creative city theory is a new approach whose milestone is the emphasis on creativity and culture in the economy and social capital. Historical contexts, in addition to having the aesthetic and identifying values of cities, are still the livelihood of millions of citizens and are therefore a good place to apply the creative city approach. The present study was conducted to explain the model of the creative city approach in the historical context of the Hamadan city, which has been done by the survey technique and the analytical-interpretive method. Initially, the evaluation indicators of the creative city, which were extracted from reliable sources, were classified into five structures: ‘socio-cultural’, ‘economic’, ‘managerial’, ‘functional-spatial’, and ‘environmental’. Then, the opinions of people and experts were collected through a questionnaire. Next, the data of the questionnaire were entered into SPSS software and with the help of the exploratory factor analysis technique which was applied separately in each of the above structures, the explanatory factors were identified. Then, using the linear multivariate regression analysis technique, the relationship between the extracted factors and the creative city approach in the historical context of Hamadan was assessed. Finally, the indicators of ‘women’s participation in social activities’, ‘the importance of knowledge-based service centers’, ‘historical events of the city’, ‘people's participation in social activities’, ‘the need to use new and knowledge-based technologies’, and ‘supporting urban entrepreneurs’ were identified. The elements have had the greatest impact on the realization of the creative city approach in the historical context of Hamedan. Introduction Historical contexts have valuable and unique features that distinguish them from other urban contexts. At the same time, this context is forced to accept changes to meet the needs of its residents, the speed of which must be commensurate with the needs of the citizens in that community. An environment that can not adapt to the needs of citizens has gradually entered a process that will lead to burnout. This confirms the need for planning and protection in historical contexts. During the industrialization of societies, the fascination with the endless use of new technology exposed many of the world’s historical contexts to serious threats and damage, some of them even perished during the effects of urban development. The exposure of today's cities to the unpredictable economic system has caused cities to rely more and more on their internal resources such as history, spaces, and creative force. The pressures of globalization and, consequently, the problems caused by the change in economic structure and the need to create a new identity have led cities to use their cultural assets to differentiate their identities and recreate the urban context (Richards & Palmer, 2010). The appropriate approach to the old and historical urban context requires a careful and comprehensive approach to the ancient context and its characteristics. Today, one of the techniques to achieve these goals is to emphasize the activities that are formed by combining different dimensions of economics and culture in a way of human logical order called creativity. Undoubtedly, historical contexts with rich cultural heritage resources are a good place to apply a creative approach. In recent decades, one of the new scientific and professional fields in the historical part of cities is the creative city approach. Methodology The present study has been done using the analytical-interpretive method based on documentary studies and field observations. In the present study, a combined method has been used, so the survey method was used as a quantitative method, and the focus group discussion method was used as a qualitative method. The conceptual framework was documented in a diagram. In the next step, extractive indices were collected in the study sample. Using factor analysis, the importance of the main factors affecting the creativity of the historical context of Hamadan was determined. Then, based on the extraction indices, a questionnaire was designed based on the Likert scale. The number of questionnaires in order to be valid for SPSS software analysis based on the Cochran sampling test was 200. The questionnaire was distributed electronically and people who have lived in the city of Hamedan and experts and professors in the field of urban planning, as well as city managers, have participated at this stage. By completing the questionnaires and entering them into SPSS software, the main factors of the creative city approach in the historical context of Hamadan city were extracted using the exploratory factor analysis method. To extract the priorities of the creative city, linear multivariate regression was performed between the extracted factors, and the coefficients of each factor and their importance were determined. Finally, the ‘creative city model in the historical context of Hamadan’ was obtained by obtaining the most important factors in this area. Discussion Factor analysis was performed using SPSS software after entering the questionnaire data in each of the 5 structures of the creative city, which were economic, environmental, institutional-governance, functional-spatial, and socio-cultural structures. According to the obtained model, the numerical value of all variables in the subscription table was more than 0.5, which indicated the appropriateness of the explanatory power of the model and the value of KMO statistics. The next output of the factor analysis was the KMO test. The KMO value is always between 0 and 1. If the value is less than 0.5, the data will not be suitable for factor analysis, and if the value is between 0.5 and 0.69, factor analysis should be done more carefully. But if this value is more than 0.7, the correlation between the data will be suitable for data analysis. On the other hand, the Bartlett test should be used to ensure that the data are suitable for factor analysis. Bartlett's test tests the hypothesis that the observed correlation matrix belongs to a society with unrelated variables. For this reason, before factor analysis, a correlation matrix between variables must be formed. If the correlation matrix is unit, it is unsuitable for factor analysis. The Bartlett test is significant when its probability is less than 0.05. After controlling and appropriateness of statistical tests that test and measure the raw data for use in factor analysis, the preliminary matrix is calculated in which the explained variance is determined by each factor. It should be noted that the specific values for all factors must be greater than 1. Then, after determining the variance of each of the factors explaining the main structures of the creative city, the factor matrix was rotated so that each of the relevant variables would have the most relationship with the factors and facilitates the conditions for naming and identifying the factors. After creating the rotated matrix of factors and using the position of variables in the structures of the creative city, the factors must be interpreted and named. After determining the main factors of the creative city approach in the historical context of Hamadan, it was necessary to understand the relationship between these factors and the realization of the creative city in the historical context. For this purpose, the linear relationship between the extracted factors and the creative city approach was investigated by the multiple linear regression method to determine the beta coefficient for the factors. Then, by multiplying the three values of ‘ load factor coefficient’, ‘factor-beta coefficient’, and ‘variable dissatisfaction rate’, the variables can be ranked as priorities for the realization of a creative city in the historical context of Hamadan. Conclusion Based on the exploratory factor analysis technique, indicators in 13 factors explaining the creative city approach including ‘historical-cultural heritage’, ‘social participation’, ‘creative class’, ‘social capital’, ‘socio-cultural diversity’, ‘creative governance’, ‘innovation’, ‘creative industries’, ‘environmental sustainability’, ‘green infrastructure’, ‘creative public space’, ‘functional diversity of space’, and ‘spatial identity structure’ were categorized. After that, the relationship between the extracted factors and the creative city approach in the historical context of Hamedan was measured by linear multivariate regression analysis technique, which revealed 4 factors, respectively, with the greatest impact: ‘historical-cultural heritage’, ‘creative class’, ‘innovation’, and ‘socio-cultural diversity’. Finally, by multiplying the three numerical values of ‘load factor coefficient’, ‘factor-beta coefficient’, and ‘average dissatisfaction’, the variables of the creative city approach were prioritized in the historical context of Hamedan, which showed the indicators of ‘women's participation in social activities in the historical context of the city’, ‘the importance of knowledge-based service centers’, ‘ historical events of the city’, ‘people's participation in social activities’, ‘the need to use new and knowledge-based technologies’, and ‘support for urban entrepreneurs’ were the most important indicators in achieving a creative city approach in the historical context of Hamedan. Citizens’ participation in urban affairs is a necessity today, which can lead to sustainable urban development. Creative urban management takes steps to solve urban problems by combining the ideas of modern urban managers with local values. Creative management strengthens the city and the socio-economic and cultural growth of citizens that people as human capital should be involved in the proper management of urban management. Achieving a creative city requires policies and programs that are coordinated and coherent, evolve, and require extensive collaboration between the public sector, at various levels of government, local and national, private sector actors, and all social institutions. For urban management to be creative, it must have a creative understanding of urban growth, urban life, and the need to engage in creative urban management while promoting its creative knowledge in the field of urban management. In such situations, it can creatively analyze urban issues and thus have a creative and innovative approach to finding ideas, and communicating with its citizens. Achieving these goals requires creating a culture and creating a suitable platform for citizenship education, the infrastructure of which is provided in the city and urban areas. In this regard, the presence of women with care and delicacy of their special look can accelerate this process. Keywords: Creative City, Historical Context, Model Explanation, Exploratory Factor Analysis, the Historical Context of Hamedan. References: - Anonymous (2010). Creative economy report 2010. The United Nations Conference on Trade and Development. - Bianchini, F., & Parkinson, M. (Eds.). (1993). Cultural policy and urban regeneration: The West European experience. Manchester: Manchester University Press. - Cohendet, P., Simon, L., Sole Parellada, F., & Valls Pasola, J. (2009). The creative city: a toolkit for urban innovators. Second Edition. London: Earthscan Publications Ltd. - Correia, C., & Oliveira, M. (2012). Creative indexes: Economic space matters?. MA Thesis in Economics. School of Economics and Business. University of Porto - Donegan, M., & Lowe, N. (2008). Inequality in the creative city: Is there still a place for ‘old-fashioned’ institutions?. Journal of Economic Development Quarterly, 22(1), 46–62. - d’Ovidio, M., & Cossu, A. (2017). Culture is reclaiming the creative city: The case of Macao in Milan, Italy. Journal of City, Culture and Society, 8, 7–12. - Ernawati, J. (2010). People ‘s impressions of a tourist-historic district. Indonesia: Brawijaya University Press. - European Commission. (2017). The cultural and creative cities monitor. Luxembourg: Publications Office of the European Union. - Evans, G. (2009). Creative cities, creative spaces and urban policy. Urban Studies, 46(5-6), 1003-1040. - Florida, R. (2002). The rise of the creative class: And how it's transforming work, leisure, community and everyday life. New York: Basic Books. - Florida, R. (2008). The rise of the creative class revisited. New York: Basic Books. - Florida, R. (2014). The creative class and economic development. Journal of Economic Development Quarterly, 28(3), 196-205. - Goldberg-Miller, S. B. (2019). Creative city strategies on the municipal agenda in New York. Journal of City, Culture and Society, 17, 26–37. - Hall, P. (2000). Creative cities and economic development. Journal of Urban Studies, 37(4), 639-649. - Kakiuchi, E. (2016). Culturally creative cities in Japan: Reality and prospects. Journal of City, Culture and Society, 7(2), 101-108. - Khoo, S. L. (2020). Towards an inclusive creative city: How ready is the historic city of George Town, Penang?. Journal of City, Culture and Society, 23, 100367. - Kotze, N., & De Vries, L. (2019). Resuscitating the African giant: Urban regeneration and inner-city redevelopment initiatives along the ‘Corridors of Freedom’ in downtown Johannesburg. Geographia Polonica, 92(1), 57–70. - Kuriakose, P. N., & Philip, S. (2021). City profile: Kochi, city-region - Planning measures to make Kochi smart and creative. Cities, 118, 103307. - Landry, C. (2010). Creativity, culture and the city: A question of inter connection. European Capital of Culture. - Montgomery, J. (2005). Beware ‘the creative class’. Creativity and wealth creation revisited. Local Economy: The Journal of the Local Economy Policy Unit, 20(4), 337–343. - Richards, G., & Palmer, R. (2010). Eventful cities: Cultural management and urban revitalisation. Routledge. - Rodrigues, M., & Franco, M. (2020). Networks and performance of creative cities: A bibliometric analysis. Journal of City, Culture and Society, 20, 100326. - Sasaki, M. (2008). Developing creative cities through networking. Journal of Policy Science, 15(3), 77-88. - Sasaki, M. (2010). Urban regeneration through cultural creativity and social inclusion: Rethinking creative city theory through a Japanese case study. Cities, 27, 3-9. - Scott, A. J. (2014). Beyond the creative city: Cognitive–cultural capitalism and the new urbanism. Journal of Regional Studies, 48(4), 565-578. - Vanolo, A. (2008). The image of the creative city: Some reflections on urban branding in Turin. Cities, 25(6), 370–382. - Vickery, J. (2011). Beyond the creative city-cultural policy in an age of scarcity. Made: A Centre for Place-Making, 1, 1-20. - Zimmerman, J. (2008). From brew, town to cool town: Neoliberalism and the creative city development strategy in Milwaukee. Cities, 25(4), 230–242.

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