عنوان مقاله [English]
Background and objectives: During the past decades, landslides have been a significant subject of research as a consequence of their devastated nature. Landslides are common geomorphic processes in mountain areas and are responsible for mass movements involving rock materials, regolith and/or soil debris. for manufacture roads, railways, water pipe line and electric line, the preparation of landslide distribution map is very much significant. Determine the occurrence of future landslides depend on the geological, geomorphological and hydrological processes that led to instability in the past and also at present. To evaluate terrain susceptibility to landslides, a number of different techniques are used, ranging from qualitative assessments based on expert judgment, which are intrinsically subjective to quantitative assessments based on advanced statistical techniques or mathematical models.
Materials and methods: the steps of methodologies that were applied in the current study, including six steps. Step1. data sources that are used in the current study including data related to field Surveys, historical reports, topographic maps of 1:50,000-scale, meteorological data, geological map of 1:100,000-scale, A digital elevation model (DEM) with the resolution of 30 m £ 30m was extracted from the ASTER GDEM data, The Landsat 8 OLI images with the resolution of 30 m × 30 m. Step2. Preparing the inventory map. In this study, a landslide inventory map with a total of 80 landslide events was provided by the extensive field survey and interpretation of aerial photos. Step3. Landslide-conditioning factors. Step4. Multicolinearity analysis of landslide conditioning factors. In the current study, 12 factors were used as conditioning factors. These include elevation, slope, plan curvature, stream length, distance from streams, topography wetness index, surface area ratio, distance from roads, lithology, distance from faults, rainfall, and land use. Step5. Combination of EBF data driven and AHP knowledge driven models according to the relation between the landslides location and the different datasets. Step5. Validation of models using AUC and SCAI inficators.
Results: Results of the spatial relationship between landslide and conditioning factors using the EBF (belief, disbelief, uncertainty, and plausibility) model are shown in Table 3. Comparison between the belief map and the disbelief map showed that belief values were high for areas where disbelief values were low and vice versa. It revealed that high potential of landslide occurrence was for the areas with high degrees of belief and low degrees of disbelief. The high uncertainty values were in the areas with low belief values. Weighting of conditioning factors by AHP showed that parameters of lithology, elevation, distance to road, slope, and rainfall are the most effective prediction factors in landslide occurrence. The consistency ratio shows 0.034 value, which is reasonably good accuracy value, which reflect the high accuracy of ranking consistency between the factors.
Conclusion : Due to some shortening of the AHP knowledge driven and EBF data driven models when applied individually in landslide susceptibility mapping, it can be overcome by using ensemble techniques. The AUC results showed that the success rate and prediction rate for ensemble model are 0.872 (78.3 %), 0.903, respectively. results of SCAI values of the ensemble model is desirable, in the high and very high susceptibility classes. The resultant landslide susceptibility map show that the high susceptibility areas are mainly distributed along the northwest to west direction in the study area. this map can provide very useful information for planners, decision makers, and engineers in slope management and land use planning in landslide areas