GISbased landslide susceptibility mapping with lo

Authors: Qingkai Meng Fang Miao Jing Zhen Xinyuan Wang An Wang Ying Peng Qiang Fan

Publish Date: 2015/09/02

Volume: 75, Issue: 3, Pages: 923-944

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Abstract

Landslides as one of the most destructive natural phenomena distribute extensively in Wolong Giant Panda Natural Reserve and cause damage to both humans and endangered species Therefore landslide susceptibility zonation LSZ mapping is necessary for government agencies and decision makers to select suitable locations for giant pandas The main purpose of this study is to produce landside susceptibility maps using logistic regression LR analytical hierarchy process AHP and a combined fuzzy and support vector machine FSVM hybrid method based on geographic information systems GIS A total of 1773 landslide scarps larger than one cell 25 × 25 m2 were selected in the landslide inventory mapping 70  of which were selected at random to be used as test data and the other 30  were used as validation Topographical geological and hydrographical data were collected processed and constructed into a spatial database Nine conditioning factors were chosen as influencing factors related to landslide occurrence slope degree aspect altitude profile curvature geology and lithology distance from faults distance from rivers distance from roads and normalized difference vegetation index NDVI Landslide susceptible areas were analyzed and mapped using the landslide occurrence factors by different methods For conventional assessment weights and rates of the affecting factors were assigned based on experience and knowledge of experts In order to reduce the subjectivity a combined fuzzy and SVM hybrid model was generated for LSZ in this paper In this approach the rates of each thematic layer were generated by the fuzzy similarity method and weights were created by the SVM method To confirm the practicality of the susceptibility map produced by this improved method a comparison study with LR AHP was assessed by means of their validation The outcome indicated that the combined fuzzy and SVM method accuracy is 8573  is better than AHP accuracy is 7884  whereas it is relatively similar to LR accuracy is 8455  The susceptibility map based on combined the fuzzy and SVM approach also shows that 58  of the study area is assigned as very highly susceptible areas and 178  of the study area is assigned as highly susceptible areasThis research is supported by the Open Fund of the Center for Earth Observation and Digital Earth the Chinese Academy of Sciences grant No 2013LDE006 We would like to thank Drs Xinyuan Wang Chuan Sheng Liu and Jing Zhen for providing various datasets and advice throughout this research

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