Prediction of landslides using ASTER imagery and data mining models |
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Authors: | Kyo-Young Song Hyun-Joo Oh Jaewon Choi Inhye Park Changwook Lee Saro Lee |
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Institution: | 1. Geological Mapping Group, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahang-no, Yuseong-Gu, Daejeon 305-350, Republic of Korea;2. Dept. of Overseas Mineral Resource, KIGAM, 92, Gwahang-no, Yuseong-gu, Daejeon 305-350, Republic of Korea;3. Geospatial Analysis & Evaluation Center, National Disaster Management Institute, 253-42, Gongdeok 2-Dong, Mapo-Gu, Seoul 121-719, Republic of Korea;4. Dept. of Geoinformatics, University of Seoul, Siripdae-gil 13, Dongdaemun-gu, Seoul 130-743, Republic of Korea;5. Geoscience Information Center, KIGAM, 124, Gwahang-no, Yuseong-gu, Daejeon 305-350, Republic of Korea |
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Abstract: | The aim of this study was to identify landslide-related factors using only remotely sensed data and to present landslide susceptibility maps using a geographic information system, data-mining models, an artificial neural network (ANN), and an adaptive neuro-fuzzy interface system (ANFIS). Landslide-related factors were identified in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. The slope, aspect, and curvature of topographic features were calculated from a digital elevation model that was made using the ASTER imagery. Lineaments, land-cover, and normalized difference vegetative index layers were also extracted from the imagery. Landslide-susceptible areas were analyzed and mapped based on occurrence factors using the ANN and ANFIS. The generalized bell-shaped built-in membership function of the ANFIS was applied to landslide susceptibility mapping. Analytical results were validated using landslide test location data. In the validation results, the ANN model showed 80.42% prediction accuracy and the ANFIS model showed 86.55% prediction accuracy. These results suggest that the ANFIS model has a better performance than does the ANN in predicting landslide susceptibility. |
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Keywords: | Landslide susceptibility ASTER ANN ANFIS GIS |
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