Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases
ABSTRACT: Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance, high mobility and strong destructive power. Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to d...
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| Format: | Article |
| Language: | English |
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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/S2096519224001083 |
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| author | Jun Sun Yu Zhuang Ai-guo Xing |
| author_facet | Jun Sun Yu Zhuang Ai-guo Xing |
| author_sort | Jun Sun |
| collection | DOAJ |
| description | ABSTRACT: Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance, high mobility and strong destructive power. Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters. This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events. Specifically, for the historical landslide cases, the landslide-induced seismic signal, geophysical surveys, and possible in-situ drone/phone videos (multi-source data collaboration) can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical (rheological) parameters. Subsequently, the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events. Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou, China gives reasonable results in comparison to the field observations. The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region (2019 Shuicheng landslide). The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide. |
| format | Article |
| id | doaj-art-395a344d151745d9879b3a8e06f2a2cc |
| institution | DOAJ |
| issn | 2589-9430 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | China Geology |
| spelling | doaj-art-395a344d151745d9879b3a8e06f2a2cc2025-08-20T02:42:04ZengKeAi Communications Co., Ltd.China Geology2589-94302024-04-017226427610.31035/cg2023138Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical casesJun Sun0Yu Zhuang1Ai-guo Xing2Guizhou Geology and Mineral Engineering Construction Co., Ltd, Guiyang 550000, ChinaState Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Corresponding author:ABSTRACT: Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance, high mobility and strong destructive power. Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters. This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events. Specifically, for the historical landslide cases, the landslide-induced seismic signal, geophysical surveys, and possible in-situ drone/phone videos (multi-source data collaboration) can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical (rheological) parameters. Subsequently, the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events. Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou, China gives reasonable results in comparison to the field observations. The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region (2019 Shuicheng landslide). The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.http://www.sciencedirect.com/science/article/pii/S2096519224001083Landslide runout predictionDrone surveyMulti-source data collaborationDAN3D numerical modelingJianshanying landslideGuizhou Province |
| spellingShingle | Jun Sun Yu Zhuang Ai-guo Xing Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases China Geology Landslide runout prediction Drone survey Multi-source data collaboration DAN3D numerical modeling Jianshanying landslide Guizhou Province |
| title | Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases |
| title_full | Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases |
| title_fullStr | Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases |
| title_full_unstemmed | Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases |
| title_short | Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases |
| title_sort | runout prediction of potential landslides based on the multi source data collaboration analysis on historical cases |
| topic | Landslide runout prediction Drone survey Multi-source data collaboration DAN3D numerical modeling Jianshanying landslide Guizhou Province |
| url | http://www.sciencedirect.com/science/article/pii/S2096519224001083 |
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