Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China
ABSTRACT: Landslide hazard mapping is essential for regional landslide hazard management. The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County, China based on an automated machine learning framework (AutoGluon). A total of 2241 landslides were ident...
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KeAi Communications Co., Ltd.
2024-04-01
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| Series: | China Geology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096519224001125 |
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| author | Tao Li Chen-chen Xie Chong Xu Wen-wen Qi Yuan-dong Huang Lei Li |
| author_facet | Tao Li Chen-chen Xie Chong Xu Wen-wen Qi Yuan-dong Huang Lei Li |
| author_sort | Tao Li |
| collection | DOAJ |
| description | ABSTRACT: Landslide hazard mapping is essential for regional landslide hazard management. The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County, China based on an automated machine learning framework (AutoGluon). A total of 2241 landslides were identified from satellite images before and after the rainfall event, and 10 impact factors including elevation, slope, aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), lithology, land cover, distance to roads, distance to rivers, and rainfall were selected as indicators. The WeightedEnsemble model, which is an ensemble of 13 basic machine learning models weighted together, was used to output the landslide hazard assessment results. The results indicate that landslides mainly occurred in the central part of the study area, especially in Hetian and Shanghu. Totally 102.44 s were spent to train all the models, and the ensemble model WeightedEnsemble has an Area Under the Curve (AUC) value of 92.36% in the test set. In addition, 14.95% of the study area was determined to be at very high hazard, with a landslide density of 12.02 per square kilometer. This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County. |
| format | Article |
| id | doaj-art-c4930a5651034efbac312df1150d65ee |
| institution | Kabale University |
| issn | 2589-9430 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | China Geology |
| spelling | doaj-art-c4930a5651034efbac312df1150d65ee2025-08-20T03:32:58ZengKeAi Communications Co., Ltd.China Geology2589-94302024-04-017231532910.31035/cg2024064Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, ChinaTao Li0Chen-chen Xie1Chong Xu2Wen-wen Qi3Yuan-dong Huang4Lei Li5National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China; Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China; Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China; Institute of Disaster Prevention, Sanhe 065201, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China; Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China; Corresponding author:National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China; Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China; Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China; School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China; Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, ChinaABSTRACT: Landslide hazard mapping is essential for regional landslide hazard management. The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County, China based on an automated machine learning framework (AutoGluon). A total of 2241 landslides were identified from satellite images before and after the rainfall event, and 10 impact factors including elevation, slope, aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), lithology, land cover, distance to roads, distance to rivers, and rainfall were selected as indicators. The WeightedEnsemble model, which is an ensemble of 13 basic machine learning models weighted together, was used to output the landslide hazard assessment results. The results indicate that landslides mainly occurred in the central part of the study area, especially in Hetian and Shanghu. Totally 102.44 s were spent to train all the models, and the ensemble model WeightedEnsemble has an Area Under the Curve (AUC) value of 92.36% in the test set. In addition, 14.95% of the study area was determined to be at very high hazard, with a landslide density of 12.02 per square kilometer. This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.http://www.sciencedirect.com/science/article/pii/S2096519224001125Landslide hazardHeavy rainfallHarzard mappingHazard assessmentAutomated machine learningShallow landslide |
| spellingShingle | Tao Li Chen-chen Xie Chong Xu Wen-wen Qi Yuan-dong Huang Lei Li Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China China Geology Landslide hazard Heavy rainfall Harzard mapping Hazard assessment Automated machine learning Shallow landslide |
| title | Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China |
| title_full | Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China |
| title_fullStr | Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China |
| title_full_unstemmed | Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China |
| title_short | Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China |
| title_sort | automated machine learning for rainfall induced landslide hazard mapping in luhe county of guangdong province china |
| topic | Landslide hazard Heavy rainfall Harzard mapping Hazard assessment Automated machine learning Shallow landslide |
| url | http://www.sciencedirect.com/science/article/pii/S2096519224001125 |
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