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GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China

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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 (F-SVM) 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 85.73 %) is better than AHP (accuracy is 78.84 %), whereas it is relatively similar to LR (accuracy is 84.55 %). The susceptibility map based on combined the fuzzy and SVM approach also shows that 5.8 % of the study area is assigned as very highly susceptible areas, and 17.8 % of the study area is assigned as highly susceptible areas.

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Acknowledgments

This 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 Dr.s Xinyuan Wang, Chuan Sheng Liu, and Jing Zhen for providing various datasets and advice throughout this research.

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Authors and Affiliations

  1. College of Geophysics, State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Key Lab of Earth Exploration & Information Techniques of Ministry of Education, Chengdu University of Technology, Chengdu, 610059, China

    Qingkai Meng & Fang Miao

  2. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China

    Jing Zhen & Xinyuan Wang

  3. College of Information Science and Technology, Chengdu University of Technology, Chengdu, 610059, China

    An Wang

  4. College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, China

    Ying Peng

  5. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China

    Qiang Fan

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  1. Qingkai Meng
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  2. Fang Miao
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  3. Jing Zhen
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  4. Xinyuan Wang
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  5. An Wang
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  6. Ying Peng
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  7. Qiang Fan
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Correspondence to Qingkai Meng.

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Meng, Q., Miao, F., Zhen, J. et al. GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China. Bull Eng Geol Environ 75, 923–944 (2016). https://doi.org/10.1007/s10064-015-0786-x

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  • DOI: https://doi.org/10.1007/s10064-015-0786-x

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