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Ensemble-based landslide susceptibility maps in Jinbu area, Korea |
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| Authors: | Moung-Jin Lee Jae-Won Choi Hyun-Joo Oh Joong-Sun Won Inhye Park Saro Lee |
| Institution: | 1. Korea Adaptation Center for Climate Change, Korea Environment Institute, 613-2 Bulgwang-Dong, Eunpyeong-Gu, Seoul, 122-706, Republic of Korea 4. Department of Earth System Sciences, Yonsei University, 134 Shinchon-dong Seodaemun-gu, Seoul, 120-749, Korea 2. Disaster Information Center, National Institute for Disaster Prevention, 135, Mapo-ro, Mapo-gu, Seoul, 121-719, Korea 3. Mineral Resources Research Department, Korea Institute of Geoscience and Mineral Resources (KIGAM), 92, Gwahang-no, Yuseong-gu, Daejeon, 305-350, Korea 5. Department of Geoinformatics, University of Seoul, Siripdae-gil 13, Dongdaemun-gu, Seoul, 130-743, Republic of Korea |
| Abstract: | Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model. |
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