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Landslides are natural disasters that cause a lot of financial and life losses in the country, annually. Identifying high risk areas can reduce the damages and be effective on land development policies. The main aim of this study was to maping the landslide hazard of Sanandaj-Kamyaran road in Kurdistan province. In current study, landslide hazard mapping were performed with two models, namely the weights of evidence (WoE), and evidential belief function (EBF). Firstly, 79 locations of landslides were obtained from field surveys. Then, the locations were categorized into two groups of 70% (55 locations) and 30% (24 locations), randomly, for modeling and validation processes, respectively. Then, 14 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, stream power index (SPI), topopgraphic wetness index (TWI), soil, river density, normalized difference vegetation index (NDVI), distance to river, distance to road, slope angle, and land use were determined to construct the spatial database. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the two achieved landslide susceptibility maps. The AUC results introduced the success rates of 0.79 and 0.89 for EBF and WoE, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the two implemented methods in identifying the regions at risk of future landslides in the study area. Finally, the results of the study showed that advanced data mining algorithms based on their structure have sufficient accuracy in spatial predicting of mass movements in the study area. Alos, it can be said that a rigorous spatial forecasting map can help managers and urban planners in identifying landslide sensitive areas for disaster management.
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