Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed,Gansu Province,China

作     者：DU Guo-liang ZHANG Yong-shuang IQBAL Javed YANG Zhi-hua YAO Xin 

作者机构：Key Laboratory of Neotectonic Movement and GeohazardInstitute of GeomechanicsChinese Academg of Geological SciencesBeijing 100081China Department of Earth SciencesAbbottabad University of Science and TechnologyAbbottabad 22010Pakistan 

出 版 物：《Journal of Mountain Science》 (山地科学学报（英文）)

年 卷 期：2017年第14卷第2期

页      面：249-268页

核心收录：

学科分类：02[经济学] 081803[工学-地质工程] 0709[理学-地质学] 0819[工学-矿业工程] 08[工学] 0303[法学-社会学] 0202[经济学-应用经济学] 020208[经济学-统计学] 0818[工学-地质资源与地质工程] 0708[理学-地球物理学] 0705[理学-地理学] 0815[工学-水利工程] 0706[理学-大气科学] 0816[工学-测绘科学与技术] 0813[工学-建筑学] 0704[理学-天文学] 0833[工学-城乡规划学] 0713[理学-生态学] 0834[工学-风景园林学（可授工学、农学学位）] 

基　　金：supported by the Project of the 12th Five-year National Sci-Tech Support Plan of China(2011BAK12B09) China Special Project of Basic Work of Science and Technology(2011FY110100-2) 

主　　题：Logistic回归模型 敏感性评价 山体滑坡 信息量法 甘肃省 中国 流域 故障距离 

摘      要：Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.