Validation of an artificial neural network model for landslide susceptibility mapping |
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| Authors: | Jaewon Choi Hyun-Joo Oh Joong-Sun Won Saro Lee |
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| Institution: | (1) Department of Earth System Sciences, Yonsei University, 262 Seongsanno, Seodaemun-Gu, Seoul, 120-749, Korea;(2) Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 92 Gwahang-no, Yuseong-gu, Daejeon, 305-350, Korea; |
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| Abstract: | The aim of this study was to validate an artificial neural network model at Youngin, Janghung, and Boeun, Korea, using the
geographic information system (GIS). The factors that influence landslide occurrence, such as the slope, aspect, curvature,
and geomorphology of topography, the type, material, drainage, and effective thickness of soil, the type, diameter, age, and
density of forest, distance from lineament, and land cover were either calculated or extracted from the spatial database and
Landsat TM satellite images. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the
artificial neural network model. The landslide susceptibility analysis results were validated and cross-validated using the
landslide locations as study areas. For this purpose, weights for each study area were calculated by the artificial neural
network model. Among the nine cases, the best accuracy (81.36%) was obtained in the case of the Boeun-based Janghung weight,
whereas the Janghung-based Youngin weight showed the worst accuracy (71.72%). |
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