| 1 | An Investigation of Landslide Susceptibility Using Logistic Regression and Statistical Index Methods in Dailekh District, Nepal显示文摘Landslide distribution and susceptibility mapping are the fundamental steps for landslide-related hazard and disaster risk management activities, especially in the Himalaya region which has resulted in a great deal of death and damage to property. To better understand the landslide condition in the Nepal Himalaya, we carried out an investigation on the landslide distribution and susceptibility using the landslide inventory data and 12 different contributing factors in the Dailekh district, Western Nepal. Based on the evaluation of the frequency distribution of the landslide, the relationship between the landslide and the various contributing factors was determined.Then, the landslide susceptibility was calculated using logistic regression and statistical index methods along with different topographic(slope, aspect, relative relief, plan curvature, altitude, topographic wetness index) and non-topographic factors(distance from river, normalized difference vegetation index(NDVI), distance from road, precipitation, land use and land cover, and geology), and 470(70%) of total 658 landslides. The receiver operating characteristic(ROC) curve analysis using 198(30%) of total landslides showed that the prediction curve rates(area under the curve, AUC) values for two methods(logistic regression and statistical index) were 0.826, and 0.823with success rates of 0.793, and 0.811, respectively. The values of R-Index for the logistic regression and statistical index methods were83.66 and 88.54, respectively, consisting of high susceptible hazard classes. In general, this research concluded that the cohesive and coherent natural interplay of topographic and non-topographic factors strongly affects landslide occurrence, distribution, and susceptibility condition in the Nepal Himalaya region. Furthermore, the reliability of these two methods is verified for landslide susceptibility mapping in Nepal’s central mountain region. | DIL Kumar RAI XIONG Donghong ZHAO Wei ZHAO Dongmei ZHANG Baojun NIRMAL Mani DAHAL WU Yanhong MUHAMMAD Aslam BAIG | 2022 | Chinese Geographical Science2022，32，5： | 2 |
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