Modeling the hillside movement in the area of Sattarkhan dam reservoir using by predictive models Logistic Regression and Neural Network

Slope instabilities are considered as one of the major dangers to human activities which occurred in natural slopes and the slopes made by humans. This study aimed to identify the factors affecting the occurrence of slope instability using logistic regression models and artificial neural network in Ahar Sattar khan dam basin. The results of statistical models to determine the potential areas of instability and ultimately create a hazard zonation map for the study area. In this regard, the most important factors in landslides such as slope, aspect, elevation, rainfall, distance from the road, fault and drainage, land use and exploration and peculiarities of each of them were identified. Standardization base of histogram model is by cutting the classes of each layer with occurred landslides. The models show that the very high-risk area in the neural network and logistic regression are 724 and 5/56 per cent respectively which cover the areas close to Sattar khan dam, mainly including the lithology of these areas located in the regions with lower resistance. Besides, Statistical methods Logistic prove that faults and lithology have an immense impact on the occurrence of landslide in this area. The ROC index value for neural network and logistic regression models are 0/85 and 0/81. So it can be said neural network model for zoning of landslides is more efficient, so any planning and construction must be compatible with the conditions of geomorphology and geology of the area leading to as least human and financial losses as possible.