Identification and zonation landslide prone areas using object oriented method and conditional probability theory (Bayesian theorem) Case Study: Ahar drainage basin South boundary (From Nasirabad to Sattar Khan dam)

Mass movements and landslides are considered the most destructive natural hazards. The study predicts landslide location using conditional probability theory (Bayesian theorem), on the southern edge of the Ahar drainage basin (From Nasirabad to Sattar Khan dam),, on the landslide occurred in the past by the object-oriented approach extracting and identifying been conducted. Using Bayesian probability theory correlation between parameters and landslide areas (two-thirds of landslide areas) to determine the weight of all categories of parameters were obtained. According to the map obtained by any of the parameters weight class, class of high silt old alluvial terraces are in layers, average pasture land in between classes, directions north and northwest, steep grade 5-25 the distance of 270-125 meters from the river greatest impact on weight and landslides in the area. The accuracy of landslide susceptibility maps using a third (12 points slip) landslide areas were evaluated. The result showed that the model with the predictability and kappa coefficient 0/93   and 0/945 percent of high and very high risk of landslides in class acceptable accuracy in evaluating and landslide susceptibility mapping.