Associate Professor, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
Researcher, Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources, Research and Education Center, AREEO, Isfahan, Iran
Document Type : Research Paper
The aim of this study is to prioritize effective factors, landslide susceptibility zonation assessment using maximum entropy (MaxEnt) and dempster shafer models in Doab Samsami watershed of Chaharmahal and Bakhtiari province. For this purpose, 15 factor maps affecting landslide occurrence as independent variables and landslide distribution map as a dependent variable were prepared and weighted using frequency ratio index (FR) and landslide distribution map in the environment ArcGIS® 10.8 . In order to implementation and validation of models, landslide distribution data were randomly divided into two categories of training and test data in the proportion of 70 and 30%, respectively. Maximum Entropy (MAXENT) and Dempster Shaffer models are performed and landslide susceptibility zonation maps are prepared and each model is divided into five very low to very high. In order to evaluate the classification accuracy and validation of the models, the frequency ratio and seed cell area index (FR&SCAI) and the area under receiver characteristic curve (AUC-ROC) were used, respectively. According to the results of the maximum entropy model, annual precipitation factors, lithology, distance to road and drainage land use are important in landslide occurrence, respectively. According to landslide susceptibility zonation maps in both models, more than 50% of landslides occurred in high and very high susceptibility categories. Finally, the validation results of the models showed that the Demester shafer model with AUC-ROC index of 0.95 and classification accuracy with higher FR & SCAI index, greater efficiency and desirability for zoning, modeling and landslide prediction in the study area.
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