Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area
<p>Rainfall is intrinsically linked to the occurrence of landslide catastrophes. Identifying the most suitable rainfall threshold model for an area is crucial for establishing effective daily landslide hazard warnings, which are essential for the precise prevention and management of local land...
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Copernicus Publications
2024-11-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/24/3991/2024/nhess-24-3991-2024.pdf |
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| author | B. Peng X. Wu |
| author_facet | B. Peng X. Wu |
| author_sort | B. Peng |
| collection | DOAJ |
| description | <p>Rainfall is intrinsically linked to the occurrence of landslide catastrophes. Identifying the most suitable rainfall threshold model for an area is crucial for establishing effective daily landslide hazard warnings, which are essential for the precise prevention and management of local landslides. This study introduces a novel approach that utilizes multilayer perceptron (MLP) regression to calculate rainfall thresholds for 453 rainfall-induced landslides. This research represents the first attempt to integrate MLP and ordinary least squares methods for determining the optimal rainfall threshold model tailored to distinct subregions, categorized by topographical and climatic conditions. Additionally, an innovative application of a three-dimensional convolutional neural network (CNN-3D) model is introduced to enhance the accuracy of landslide susceptibility predictions. Finally, a comprehensive methodology is developed to integrate daily rainfall warning levels with landslide susceptibility predictions using a superposition matrix, thus offering daily landslide hazard warning results for the study area. The key findings of this study are as follows. (1) The optimal rainfall threshold models and calculation methods vary across different subregions, underscoring the necessity for tailored approaches. (2) The CNN-3D model substantially improves the accuracy of landslide susceptibility predictions. (3) The daily landslide hazard warnings were validated using anticipated rainfall data from 19 July 2020, thereby demonstrating the reliability of both the landslide hazard warning results and the rainfall threshold model. This study presents a substantial advancement in the precise prediction and management of landslide hazards by employing innovative modeling techniques.</p> |
| format | Article |
| id | doaj-art-ca2139d3d11c4267a0d1484458fc33c7 |
| institution | Kabale University |
| issn | 1561-8633 1684-9981 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj-art-ca2139d3d11c4267a0d1484458fc33c72024-11-25T10:57:12ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812024-11-01243991401310.5194/nhess-24-3991-2024Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir areaB. Peng0X. Wu1School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaSchool of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China<p>Rainfall is intrinsically linked to the occurrence of landslide catastrophes. Identifying the most suitable rainfall threshold model for an area is crucial for establishing effective daily landslide hazard warnings, which are essential for the precise prevention and management of local landslides. This study introduces a novel approach that utilizes multilayer perceptron (MLP) regression to calculate rainfall thresholds for 453 rainfall-induced landslides. This research represents the first attempt to integrate MLP and ordinary least squares methods for determining the optimal rainfall threshold model tailored to distinct subregions, categorized by topographical and climatic conditions. Additionally, an innovative application of a three-dimensional convolutional neural network (CNN-3D) model is introduced to enhance the accuracy of landslide susceptibility predictions. Finally, a comprehensive methodology is developed to integrate daily rainfall warning levels with landslide susceptibility predictions using a superposition matrix, thus offering daily landslide hazard warning results for the study area. The key findings of this study are as follows. (1) The optimal rainfall threshold models and calculation methods vary across different subregions, underscoring the necessity for tailored approaches. (2) The CNN-3D model substantially improves the accuracy of landslide susceptibility predictions. (3) The daily landslide hazard warnings were validated using anticipated rainfall data from 19 July 2020, thereby demonstrating the reliability of both the landslide hazard warning results and the rainfall threshold model. This study presents a substantial advancement in the precise prediction and management of landslide hazards by employing innovative modeling techniques.</p>https://nhess.copernicus.org/articles/24/3991/2024/nhess-24-3991-2024.pdf |
| spellingShingle | B. Peng X. Wu Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area Natural Hazards and Earth System Sciences |
| title | Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area |
| title_full | Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area |
| title_fullStr | Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area |
| title_full_unstemmed | Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area |
| title_short | Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area |
| title_sort | optimizing rainfall triggered landslide thresholds for daily landslide hazard warning in the three gorges reservoir area |
| url | https://nhess.copernicus.org/articles/24/3991/2024/nhess-24-3991-2024.pdf |
| work_keys_str_mv | AT bpeng optimizingrainfalltriggeredlandslidethresholdsfordailylandslidehazardwarninginthethreegorgesreservoirarea AT xwu optimizingrainfalltriggeredlandslidethresholdsfordailylandslidehazardwarninginthethreegorgesreservoirarea |