.Application of artificial neural network in the evaluation and landslide hazard zonation

Landslide is one of the natural hazards in mountainous regions that results in huge losses every year. Alashtar Doab watershed with mountainous terrains, uplands and different natural conditions has the potential for landslide. The purpose of this study is landslide hazard zoning using artificial neural network model in Alashtar Doab watershed. In order to preparing the map, first of all parameters of the landslide were extracted and then the layers were prepared and after that a landslide distribution map that was occurred in the basin was prepared and then by combining landslide influencing factors with landslide distribution map, the impact of each of these factors such as slope, aspect, lithology, rainfall, land use, distance from fault and stream in ArcGIS software were measured. In this research, artificial neural network model with error back propagation algorithm and sigmoid activation function was used. The final structure of the network consisted of eight neurons in the input layer, eleven neurons in the hidden layer and one neuron in the output layer. Network accuracy in the testing phase was calculated by 85.93 percentages. After optimization of the network structure, all area information was imported to the network. Based on landslide hazard zoning using artificial neural network model, 37.44, 45.7, 93.8, 49.32 and 76.6 percent of the area at risk is located in very low, low, medium, high and very high classes, respectively.