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Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China

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Abstract

The main purpose of this study is to produce reliable susceptibility maps using GIS-based support vector machine (SVM) models and compare their performances for the Qianyang County of Baoji City, Shaanxi Province, China. In this paper, with kernel classifiers of linear, polynomial, radial basis function and sigmoid, the four various types were applied in landslide susceptibility mapping. The important input parameters for the landslide susceptibility assessment were acquired from different sources. Firstly, 81 landslide sites were obtained by aerial photographs, earlier reports and field surveys. Then, the landslide inventory was randomly classified into two datasets: 70 % (56 landslides) for training the models and 30 % (25 landslides) for validation purpose. Secondly, 15 landslide conditioning factors were selected (i.e., slope angle, slope aspect, altitude, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, NDVI, STI, SPI, TWI, geomorphology, rainfall, and lithology). Subsequently, with four types of kernel function classifiers based on landslide conditioning factors, landslide susceptibility parameters were obtained using SVM models. Finally, the rationality of landslide susceptibility maps was verified using the receiver operating characteristics with both success rate curve and prediction rate curve. The validation results showed that success rates for the four SVM models were 83.15 % (RBF-SVM), 82.72 % (PL-SVM), 81.77 % (LN-SVM), and 79.99 % (SIG-SVM). The prediction rates for the four SVM models were 77.98 % (RBF-SVM), 77.50 % (PL-SVM), 77.07 % (LN-SVM), and 76.08 % (SIG-SVM), respectively. The results showed that the RBF-SVM model had the highest overall performance.

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Acknowledgments

The authors want to express their gratitude to anonymous reviewers and editors for their valuable comments which were very useful in bringing the manuscript into the present form. The study is supported by the National Science Foundation of China (Grant No. 41302276) and Natural Science Foundation of Shaanxi Province (Grant No. 2012JM5008).

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Authors and Affiliations

  1. School of Geology and Environment, Xi’an University of Science and Technology, Xi’an, 710054, China

    Wei Chen & Zhou Zhao

  2. School of Resources and Earth Science, China University of Mining and Technology, Xuzhou, 221116, China

    Huichan Chai & Qiqing Wang

  3. Jiangxi Provincial Meteorological Observatory, Jiangxi Meteorological Bureau, Nanchang, 330046, China

    Haoyuan Hong

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  1. Wei Chen
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  2. Huichan Chai
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Correspondence to Wei Chen.

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Chen, W., Chai, H., Zhao, Z. et al. Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China. Environ Earth Sci 75, 474 (2016). https://doi.org/10.1007/s12665-015-5093-0

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