|Occurrence of numerous floods in different regions of the country has always caused a lot of damages in various fields. Therefore, it seems necessary to prepare and compile a comprehensive plan in the field of flood control. The study area is influenced by the Mediterranean climate and within the radius of the Caspian and Caucasian climates. Due to the high altitude differences, it has a variety of climates and high variability in rainfall, and known as one of the areas exposed to destructive floods. The purpose of this study is to identify flood prone areas based on multi-criteria decision making and neural network model in Khodaafarin Watershed. For this purpose, according to the factors affecting the occurrence of floods, the information layers of the region including Curve Number (CN), Gravilius coefficient, runoff height, precipitation, distance from waterway, soil retention, waterways, slope, drainage density, geology and vegetation, according to the study of maps, reports, satellite images and field surveys. In order to weight the criteria in the present study, network analysis method (ANP) and Super Decisions software were used. The factor of runoff height with the amount of 0.241, slope with the amount of 0.207 and precipitation with the weight of 0.169 were the most important in relation to flood occurrence. Finally, by combining these layers according to their weight in the GIS environment, a flood risk zoning map was extracted in five classes. The results also showed that, 43 square kilometers (3% of the area) of the watershed is in the very high flood risk class and 288 square kilometers (18% of the area) in the high flood risk calss. More than 21% of the area is among the areas with high and very high flood potential. Therefore, it seems that the need for surface water management in the region in order to prevent floods and the proper use of water in the region is necessary.|
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