Proceedings of the Korean Geotechical Society Conference (한국지반공학회:학술대회논문집)
- 2002.10a
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- Pages.499-506
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- 2002
Development of Artificial Neural Network Techniques for Landslide Susceptibility Analysis
산사태 취약성 분석 연구를 위한 인공신경망 기법 개발
- Chang, Buhm-Soo (R&D division, Korea Infrastructure Safety and Technology Corporation) ;
- Park, Hyuck-Jin (R&D division, Korea Infrastructure Safety and Technology Corporation) ;
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Lee, Saro
(National Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
- Juhyung Ryu (Dept. of Earth System Sciences, Yonsei University) ;
- Park, Jaewon (National Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
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Lee, Moung-Jin
(Dept. of Earth System Sciences, Yonsei University)
- Published : 2002.10.01
Abstract
The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the newly developed techniques for assessment of landslide susceptibility to the study area of Yongin in Korea. Landslide locations were identified in the study area from interpretation of aerial Photographs and field survey data, and a spatial database of the topography, soil type and timber cover were constructed. The landslide-related factors such as topographic slope, topographic curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter were extracted from the spatial database. Using those factors, landslide susceptibility and weights of each factor were analyzed by two artificial neural network methods. In the first method, the landslide susceptibility index was calculated by the back propagation method, which is a type of artificial neural network method. Then, the susceptibility map was made with a GIS program. The results of the landslide susceptibility analysis were verified using landslide location data. The verification results show satisfactory agreement between the susceptibility index and existing landslide location data. In the second method, weights of each factor were determinated. The weights, relative importance of each factor, were calculated using importance-free characteristics method of artificial neural networks.
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