University of Isfahan Geography and Environmental Planning 2008-5362 29 4 2019 02 20 Comparing Three Main Methods of Artificial Intelligence in Flood Estimation in Yalphan Catchment Comparing Three Main Methods of Artificial Intelligence in Flood Estimation in Yalphan Catchment 35 50 23168 10.22108/gep.2018.98036.0 FA Hamid Nouri Assistant Professor, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran Alireza Ildoromi Assistant Professor, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran Mahdi Sepehri MA Student, Faculty of Natural Resources and Environment Malayer University, Malayer, Iran Mahdi Artimani MA Student, Faculty of Natural Resources and Environment, University of Lorestan, Khoramabad, Iran Journal Article 2015 06 09 Estimation of discharge as one of the major issues in water resource management and flood control has a key role in the success of water construction design and efficiency of Bio-Mechanical proceeding in catchments. In this research, discharge Peak of Yalphan River has been simulated using three main methods of artificial intelligence (MLP neural network model, subtractive clustering and ANFIS model, and the combination of neural network and genetic algorithm). For this purpose, 8 parameters have been prepared as input data (2001-2012) including precipitation in the event day, precipitations during 5 days before the event day, base flow in the event day and CN map. Peak of flow has been considered as output data of models. RSME, MAE and NSE indicators has been used to assess the artificial intelligence models. Output data of neural network model have been imported to the combined model of neural network and genetic algorithm. Also, output data of subtractive clustering model have been imported to ANFIS model. Finally three models have been assessed using the mentioned indicators. The results showed that the combined model of neural network and genetic algorithm is better than the other models in Yalphan Catchment. Estimation of discharge as one of the major issues in water resource management and flood control has a key role in the success of water construction design and efficiency of Bio-Mechanical proceeding in catchments. In this research, discharge Peak of Yalphan River has been simulated using three main methods of artificial intelligence (MLP neural network model, subtractive clustering and ANFIS model, and the combination of neural network and genetic algorithm). For this purpose, 8 parameters have been prepared as input data (2001-2012) including precipitation in the event day, precipitations during 5 days before the event day, base flow in the event day and CN map. Peak of flow has been considered as output data of models. RSME, MAE and NSE indicators has been used to assess the artificial intelligence models. Output data of neural network model have been imported to the combined model of neural network and genetic algorithm. Also, output data of subtractive clustering model have been imported to ANFIS model. Finally three models have been assessed using the mentioned indicators. The results showed that the combined model of neural network and genetic algorithm is better than the other models in Yalphan Catchment. https://gep.ui.ac.ir/article_23168_20c9d9e3b116308df1f83ba31b6d8a19.pdf