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Validation of an artificial neural network model for landslide susceptibility mapping

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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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Acknowledgments

This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Knowledge Economy of Korea.

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Authors and Affiliations

  1. Department of Earth System Sciences, Yonsei University, 262 Seongsanno, Seodaemun-Gu, Seoul, 120-749, Korea

    Jaewon Choi, Hyun-Joo Oh & Joong-Sun Won

  2. Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 92 Gwahang-no, Yuseong-gu, Daejeon, 305-350, Korea

    Saro Lee

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  1. Jaewon Choi
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  2. Hyun-Joo Oh
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  3. Joong-Sun Won
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  4. Saro Lee
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Correspondence to Saro Lee.

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Choi, J., Oh, HJ., Won, JS. et al. Validation of an artificial neural network model for landslide susceptibility mapping. Environ Earth Sci 60, 473–483 (2010). https://doi.org/10.1007/s12665-009-0188-0

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  • DOI: https://doi.org/10.1007/s12665-009-0188-0

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