نویسندگان

چکیده

زمین‌لغزش‌ها در ایران ازجمله بلایای طبیعی هستند که مورفولوژی را به‌طور ناگهانی برهم می‌زنند و منجربه خسارات مالی و جانی زیادی می‌شوند. نظربه تأثیرات سویی که زمین‌لغزش‌ها بر سکونت‌گاه‌های روستایی و شهری و منابع طبیعی دارند شناسایی مناطق مستعد وقوع زمین‌لغزش امری بسیار ضروری برای جلوگیری از چنین خسارت‌هایی بشمار می‌آید. هدف تحقیق حاضر برقراری ارتباط بین زمین‌لغزش و فاکتورهای محیطی تأثیرگذار در وقوع آن و تهیه نقشه حساسیت زمین‌لغزش، با استفاده از روش ارزیابی چندمعیاره مکانی است. نقشه عوامل مختلف با استفاده‏ از تصاویر ماهواره‌ای، نقشه‌های موضوعی و بازدیدهای صحرایی تهیه‌شده و درختواره عوامل و محدودیت‌ها در نرم‌افزار ILWIS طراحی گردید. نقشه‌های زمین‌شناسی، شیب، کاربری اراضی و فاصله از جاده، رودخانه و فاصله از چشمه به‌عنوان عوامل و نقشه شیب به‌عنوان محدودیت به سیستم معرفی شدند. پس از وزن‌دهی به عوامل و زیر عوامل، نقشه حساسیت به زمین‌لغزش منطقه به‌دست‌آمد. با ارائه این نقشه بـه برنامـه‌ریـزان در مـدیریت بحران و تخفیف نگرانی عمومی در سیاستهای توسعه اراضی کمک می‌شود.

کلیدواژه‌ها

عنوان مقاله [English]

Landslide Risk Zoning Using Spatial Multi-Criteria Evaluation Method (SMCE) (Case study: Watershed of Benvagheh in Chaharmahal Bakhtiari Province)

نویسندگان [English]

  • A.R Tayeba
  • A.A. Jamali
  • M. Dadashi
  • F. Noorbakhsh
  • A.A. Jamali
  • A. Hasanabadi

چکیده [English]

1-Introduction

Landslides are natural geological phenomena that can cause fatalities and property damage (4). According to the published statistics, in 1990-2005, the landslides made 5% of all natural disasters (5). Due to the growing trend of urban developments in mountainous regions and due to the socioeconomic consequences of landslides, it is of great importance to locate those parts of the earth that are vulnerable to landslides. (6)
Reminding the readers of the fact that the past is the key to the future, conducting research on landslides and factors that cause them , can serve as a key to know how they reoccur if similar causal factors exist (2). Accessibility of a wide range of précised spatial data and computers of high ability for processing, has made it possible for us to analyze these data (Landslides that occurred in the past and the factors that caused them) under GIS and ILWIS environment and produce the landslide vulnerability map (1).
Various methods have been suggested in the recent years for preparing landslide vulnerability map. “Regression Models”, “Artificial Nervous Systems”, and “Weights of Evidence” are the most applicable methods of all and each has its own special weaknesses and strengths.
Most of these methods are qualitative innovations using which an expert can score the landslides factors on the basis of the past experiences and draws the zoning map based on the total scores(15). The reliability of zoning map depends on the quality of the accessible data, the study scale and on the selection of a proper method (13).
However, when using those statistical methods that can encompass two or more variables, the distribution map of the landslides plays a key role. In these methods, the pertinent factors and the level of each is weighed based on the frequency of landslides and the vulnerability is calculated by summing up of all weights at each certain work units (9,11) .
For instance, using a multivariable statistical method, zoning was carried out in Shalmanroud in Gilan province/Iran at the scale of 1:50000 using GIS system (8). The layers included were: Slope, land application, lithology , and raining. The authenticity of the map prepared using landslides distribution map, the proper implementation of the prepared model at the watershed nearby, and the authenticity of the procedure followed to prepare the zonation map, were assessed and the latter map was checked against the landslide distribution map, which had satisfactory results.
Some researchers assessed AHP model results for zoning Aharchai Watershed vulnerability (7). The research findings proved that geological factors play the most significant role while human factors play the least role in the landslides of this region.
In recent years, the geographical information system is used in combination with Spatial Multivariable Evaluation to solve the problems in order to help the managers and decision makers. Spatial Multivariable Evaluation is a useful method to identify proper locating solutions and to compare these solutions based on multiple factors combination, and it can be displayed within the least period of time using information layers. This method reveals the ability of the geographical information system in managing and processing spatial data and also conveys the flexibility of Spatial Multivariable Evaluation in combining spatial data (such as the soil type, the slope, etc.) with the weights allocated by the pertinent experts to different variables . Spatial Multivariable Evaluation is a method to recognize and present comparative solutions to the spatial problems at the time of combining the multiple factors that will ultimately be shown in the map (12).
Shahrekord is located in central part of Iran and it is the central city of Chaharmahal - Bakhtiari Province. It is located 97 kilometers from Isfahan and to the west of the city. The former name was Dehkord changed to Shahrekord in September 1935 when the place became a city officially. According to the statistics of 2006, the population of the central and old regions of the city was 131612 and the population of the city including those of the four regions of Manzarieh estate, Chaleshtar, Eshkaftak and Mahdieh equals 148464.



Figure 1:Map Showing the Location of Watershed of Bon county in Chaharmahal - Bakhtiari Province.

2- Methodology

The research methods and stages are specified below in brief:

First stage: Collection of all regional data including the statistics, the maps and the required information tables and preparing them by establishing boundary around spatial factors and preparing data to be used as input in ILWIS software.Second stage: Designing of a Spatial Multi Criteria Evaluation Model in the form of a tree-like diagram of criteria under SMCE environment of ILWIS software.

Third stage: Standardization of the factors under SMCE environment of ILWIS software and allocating weight to the factors and criteria under the software environment.

Fourth stage: Combining the prepared data and producing a composite index map.

Fifth stage: Landslide Risk Zoning using the composite index map based on a column diagram of map pixels value. It is of great significance to use proper information layers for the purpose of landslide risk zoning. Hence, field and library studies were conducted in the site to identify the main spatial factors and factors that limit landslide happening. These factors include the slope of the region, type of land application, geological units, distance from the road, fountain and river.
Spatial Multivariable Evaluation Model was used to zone the landslide risk and the tree-like diagram of spatial factors was designed for Shahrekord watershed based on the major goal of the research , that is, the zoning of the landslide risk. All prepared maps including the linear, the dotted and the polygonal ones, were applied in the designed model after being changed into networks. This is illustrated in figure 2 of the tree-like diagram.

Figure 2: Tree-like diagram of Spatial Multivariable Evaluation of Landslide Risk Zoning for the Watershed of Bon County in Chaharmahal & Bakhtiari Province.
Information categories: In this paper, the information categories including the slope, lithology , land application, fault, distance from road, fountain and waterway were prepared in Arc GIS 10 software after studying various influential factors. A description of these 6 factors having been identified as the main factors is conveyed below.
Slope: It is one of the most important parameters as far as the mountain skirt resistance is concerned. The slope map of the site under study, was prepared using digital topographic maps at the scales of 1:25000 (state topography organization, 1993) and 1:50000 (Military Forces Geography Organization, 1999) and using Topo to Raster functions in order to develop the maps of digital elevation model and slope under ArcGIS software environment. The map is divided into 5 tiers. Since the highest slope is that of the regions the constituents of which are vulnerable to sliding and proper for the formation of the soil belonging to the category of 0.75 to 1 (unstable), hence such soils can be of the highest potential for the landslide. On the other hand, notwithstanding the soil category of 0.75 to 1, slopes of higher degrees cause higher vulnerability.
Lithology: This factor is a key parameter in sliding, because different lithological units show different vulnerability to geomorphologic phenomena. The lithological map of the place was prepared after examining the geological maps, satellite data and pictures and field measurement. The lithological studies proved that the place is vulnerable to sliding even in low slopes due to its soil being severely oxidized and for the low permeability of the soil and the great volume of water that remains in it, also because of insufficient plantation.
Distance from Road: The map showing the distance from the road, was prepared using digital topographic maps of 1:250000 issued by the state topography organization and by applying distance functions and through reclassification and uniting it with landslides distribution map under ArcGIS software environment. Next, under ArcGIS environment, the distances from the road were classified under 5 categories. Based on the pertinent map, the more the distance from the road is, the lesser the landslide hazard will be.
Distance from Fault: To prepare the regional faults map, satellite data were used under ENV14.7 software environment. Through reproduction of the pictures, the main and the auxiliary faults of the region were identified. Spatial filters are used to make the pictures clear. According to the pertinent maps, regions that are farther from the faults are less vulnerable to land sliding. The study place on the other hand does not encompass any fault line.
Distance from waterway: Because of the skirts being eroded and stones being oxidized and because of the impacts these two have on the content of the skirt water, waterways system has a significant role in instability of the slopes. The map showing the distance from the waterway was hence prepared just like the road map, using digital topographic maps of 1:250000 and through applying distance functions and via reclassification and uniting it with landslides distribution map under ArcGIS software environment. Based on the pertinent map, the category of 0.75 to 1 has the highest vulnerability to sliding. Generally, regions that are farther from the waterway have lower potential for sliding.
Land Application & Plantation: Since, the type of plantation and the way the lands are used, affect the sliding phenomenon, the plantation map of the region was prepared using topographic maps of 1:25000 issued by the state topography organization and through field measurement. Based on the findings, due to the highest potential of sliding belong to agricultural fields due to their poor range and because of the irrigation and drainage activities that prevail in these areas. Considering the fact that the maps can be different both in contents and in features, and since some may display descriptive specifications such as remoteness or proximity while others may contain digits like zero, 1, ten or the likes, it is essential for the maps to be standardized. In this system, all specifications of the maps are standardized by using a digit range of 0 to 1. To standardize the maps that enter into SMCE, a software standardization method including Boolean and Class Input Methods were applied.To weigh the standardized maps of the factors and sub-factors, Pairwise comparison and direct ranking methods were applied. Weighing implies the importance of each criterion compared with other criteria. It should be noted that inadaptability rate should not exceed 0.1 .Figure 3showe that Landslide Risk Classified Map Achieved through Combining Input Maps Including Factors and Limitations.

Figure 3: The Composite Index Map of the Watershed of Bon County in Chaharmahal & Bakhtiari Province
3– Discussion
The final map (figure 3) reveals that regions that are highly vulnerable to sliding are located near main crowded roads and fountains. As it was said under “Materials & Methods” , as the distance from the roads increases, the potential for sliding decreases. The same is with waterways and fountains.
4– Conclusion
To improve the study and considering the final map of the landslides (13) , three parts of the region were selected on a random basis to convey the impact of each factor on vulnerability to sliding. This map (figure 3) has specified the locations to allow better resistance against natural disasters. Through this map, planners are assisted to manage crisis.Compared with a research conducted by Nafouti, 2010, in Shalmanroud in east of Gilan province, this allows a more efficient resistance against the risk of sliding, although even the said research focused on the spatial limitations and factors in Shalmanroud watershed and presented a map about the zones with potentiality of land sliding. SMCE has a high potential for acting cost effectively at a short time and it also increases the accuracy of making decision about identification of places that are vulnerable to sliding. It also provides managers with a proper framework to make policies regarding land development and prevent land application in highly vulnerable regions. It also allows performing of correcting measures such as drainage and pile driving in highly vulnerable regions to prevent fatalities and property damages.

کلیدواژه‌ها [English]

  • spatial multi-criteria evaluation
  • Zoning
  • landslide risk
  • spatial multi
  • criteria evaluation
  • ILWIS