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Soft computing in assessment of earthquake-triggered landslide susceptibility

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Abstract

This study compared two soft computing methods that are applied to soft computation in assessment of earthquake-triggered landslide susceptibility: the support vector machine (SVM) and artificial neural networks (ANN). As a case study, a series of landslide susceptibility maps were constructed for the affected area of the 2013 Minxian-Zhangxian, Gansu Province, China Mw 5.9 earthquake. From data available, 2330 coseismic landslides were partitioned into two subsets which were used as a training dataset and a test dataset, respectively. In addition, other 1631 points on the map were randomly selected as non-landslide samples in those areas were not influenced by the coseismic landslides. Ten conditioning factors of landslides were considered, including elevation, relative relief, slope angle, slope aspect, slope curvature, slope position, lithology, peak ground acceleration (PGA), distance from the probable seismogenic fault, and distance along the fault. Using the ANN and SVM, ten landslide susceptibility maps were produced for the study area. Cross comparisons and validations of these ten resulting maps with real coseismic landslides show that the polynomial kernel type with a high enough polynomial degree term value (e.g., 5 or 6) of the SVM technology is most appropriate for coseismic landslide susceptibility assessment, which is a challenge to the previously held notion that the SVM method with radial basis function is the most suitable.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (41472202) and Key Laboratory for Geo-hazards in Loess area, MLR (KLGLAMLR2014003).

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Authors and Affiliations

  1. Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration, 1# Huayanli, Chaoyang District, PO Box 9803, Beijing, 100029, China

    Chong Xu

  2. Key Laboratory of Geo-hazards in Loess Area, Xi’an Center of Geological Survey, China Geological Survey, Xi’an, China

    Chong Xu & Genlong Wang

  3. Beijing Meteorological Information Center, Beijing Meteorological Service, Beijing, China

    Lingling Shen

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  1. Chong Xu
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Correspondence to Chong Xu.

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Xu, C., Shen, L. & Wang, G. Soft computing in assessment of earthquake-triggered landslide susceptibility. Environ Earth Sci 75, 767 (2016). https://doi.org/10.1007/s12665-016-5576-7

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