Landslide susceptibility mapping in Bijar city, Kurdistan Province, Iran: a comparative study by logistic regression and AHP models

Abstract Landslides and instability slopes are major risks for human activities which often lead to losing economic resources and damaging properties and structures. The main aims of this study are identifying the underlying effective factors of landslide occurrence in Bijar, Kurdistan Province, and evaluating the regions prone to landslide to prepare the susceptibility map using the logistic regression (LR) and analytical hierarchy process (AHP). At first, using field surveys, questionnaires, geological and topographic maps and reviewing the related studies, ten effective factors including the elevation of sea level, slope inclination, slope aspect, geology, distance from the linear elements (fault, road, and river), precipitation and land use were recognized. Then, they were processed using ARC GIS 10 and ILWIS 33. The dependent variable included 144 of slopes prone to landslide selected across the region as the landslide data (code 1), and also 144 stable landslide slopes were randomly selected as landslide free data (code 0). The results of the evaluation showed that LR model with PCPT index equals to 83.4; −2LL index equals to 229.226; and ROC index equals to 98.5% and landslide susceptibility map based on SCAI index had high verification in the case study. Therefore, 75.489% of the area had very low susceptibility, 10.037% low susceptibility, 3.628% moderate susceptibility, 4.062% high susceptibility and 6.784% very high susceptibility. Based on the preferences of the AHP method, the weighting of selected parameters was logically performed so that the parameters could be arranged according to their priorities. The results of the AHP model showed that 3.4% of the area had very low susceptibility, 30.43% low susceptibility, 46.68% moderate susceptibility, 18.14% high susceptibility, and 1.33% very high susceptibility.

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