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Sustainability 2017, 9(1), 48;

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon 305350, Korea
Department of Geophysical Exploration, Korea University of Science and Technology, Daejeon 305350, Korea
Department of English Language and Literature, University of Seoul, Seoul 02504, Korea
Department of Geoinformatics, University of Seoul, Seoul 02504, Korea
Author to whom correspondence should be addressed.
Academic Editor: Vincenzo Torretta
Received: 13 September 2016 / Revised: 17 December 2016 / Accepted: 23 December 2016 / Published: 1 January 2017
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In this study, the support vector machine (SVM) was applied and validated by using the geographic information system (GIS) in order to map landslide susceptibility. In order to test the usefulness and effectiveness of the SVM, two study areas were carefully selected: the PyeongChang and Inje areas of Gangwon Province, Korea. This is because, not only did many landslides (2098 in PyeongChang and 2580 in Inje) occur in 2006 as a result of heavy rainfall, but the 2018 Winter Olympics will be held in these areas. A variety of spatial data, including landslides, geology, topography, forest, soil, and land cover, were identified and collected in the study areas. Following this, the spatial data were compiled in a GIS-based database through the use of aerial photographs. Using this database, 18 factors relating to topography, geology, soil, forest and land use, were extracted and applied to the SVM. Next, the detected landslide data were randomly divided into two sets; one for training and the other for validation of the model. Furthermore, a SVM, specifically a type of data-mining classification model, was applied by using radial basis function kernels. Finally, the estimated landslide susceptibility maps were validated. In order to validate the maps, sensitivity analyses were carried out through area-under-the-curve analysis. The achieved accuracies from the SVM were approximately 81.36% and 77.49% in the PyeongChang and Inje areas, respectively. Moreover, a sensitivity assessment of the factors was performed. It was found that all of the factors, except for soil topography, soil drainage, soil material, soil texture, timber diameter, timber age, and timber density for the PyeongChang area, and timber diameter, timber age, and timber density for the Inje area, had relatively positive effects on the landslide susceptibility maps. These results indicate that SVMs can be useful and effective for landslide susceptibility analysis. View Full-Text
Keywords: landslide; GIS; SVM; validation; sensitivity analysis landslide; GIS; SVM; validation; sensitivity analysis

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Lee, S.; Hong, S.-M.; Jung, H.-S. A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea. Sustainability 2017, 9, 48.

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