Insight into data-driven model for the formation mechanism and causative factors of landslides induced by the 2013 Tianshui extreme rainfall event
The 2013 extreme rainfall-induced landslide in the Tianshui area was the most severe geological disaster since 1984. This study aims to improve the understanding of landslide formation mechanisms by using a data-driven model to predict landslide probabilities under various rainfall scenarios. A prob...
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Taylor & Francis Group
2025-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2025.2487826 |
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| author | Siyuan Ma Xiaoyi Shao Chong Xu |
| author_facet | Siyuan Ma Xiaoyi Shao Chong Xu |
| author_sort | Siyuan Ma |
| collection | DOAJ |
| description | The 2013 extreme rainfall-induced landslide in the Tianshui area was the most severe geological disaster since 1984. This study aims to improve the understanding of landslide formation mechanisms by using a data-driven model to predict landslide probabilities under various rainfall scenarios. A probability evaluation model was constructed based on total rainfall and landslide data, with uncertainty quantified through 20 model runs. The results show consistent trends in predicted landslide probability across five rainfall scenarios, with high probability areas primarily located on valley sides. As rainfall accumulates, the areas with low probability (<0.005) decrease, while the areas with moderate probability (0.01–0.02) increase. Further analysis of various influencing factors on landslides shows that the hillslope gradient has the highest regression coefficient, followed by the topographic wetness index (TWI), indicating that steeper slopes and higher TWI values are linked to a higher likelihood of landslides. Conversely, elevation and relief show negative coefficients, suggesting that areas with lower elevation and relief are more susceptible to landslides. Due to the unique terrain and lithological characteristics of the region, it is highly prone to landslides, with rainfall as a triggering factor contributing only marginally to their occurrence. |
| format | Article |
| id | doaj-art-c445b9a47e7646f2a24c80f1b8dbcf2d |
| institution | OA Journals |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-c445b9a47e7646f2a24c80f1b8dbcf2d2025-08-20T02:26:07ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132025-12-0116110.1080/19475705.2025.2487826Insight into data-driven model for the formation mechanism and causative factors of landslides induced by the 2013 Tianshui extreme rainfall eventSiyuan Ma0Xiaoyi Shao1Chong Xu2Institute of Geology, China Earthquake Administration, Beijing, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, ChinaNational Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, ChinaThe 2013 extreme rainfall-induced landslide in the Tianshui area was the most severe geological disaster since 1984. This study aims to improve the understanding of landslide formation mechanisms by using a data-driven model to predict landslide probabilities under various rainfall scenarios. A probability evaluation model was constructed based on total rainfall and landslide data, with uncertainty quantified through 20 model runs. The results show consistent trends in predicted landslide probability across five rainfall scenarios, with high probability areas primarily located on valley sides. As rainfall accumulates, the areas with low probability (<0.005) decrease, while the areas with moderate probability (0.01–0.02) increase. Further analysis of various influencing factors on landslides shows that the hillslope gradient has the highest regression coefficient, followed by the topographic wetness index (TWI), indicating that steeper slopes and higher TWI values are linked to a higher likelihood of landslides. Conversely, elevation and relief show negative coefficients, suggesting that areas with lower elevation and relief are more susceptible to landslides. Due to the unique terrain and lithological characteristics of the region, it is highly prone to landslides, with rainfall as a triggering factor contributing only marginally to their occurrence.https://www.tandfonline.com/doi/10.1080/19475705.2025.24878262013 Tianshui rainfall eventlogistic regression (LR) modelrainfall-induced landslidessusceptibility assessmentinfluencing factors |
| spellingShingle | Siyuan Ma Xiaoyi Shao Chong Xu Insight into data-driven model for the formation mechanism and causative factors of landslides induced by the 2013 Tianshui extreme rainfall event Geomatics, Natural Hazards & Risk 2013 Tianshui rainfall event logistic regression (LR) model rainfall-induced landslides susceptibility assessment influencing factors |
| title | Insight into data-driven model for the formation mechanism and causative factors of landslides induced by the 2013 Tianshui extreme rainfall event |
| title_full | Insight into data-driven model for the formation mechanism and causative factors of landslides induced by the 2013 Tianshui extreme rainfall event |
| title_fullStr | Insight into data-driven model for the formation mechanism and causative factors of landslides induced by the 2013 Tianshui extreme rainfall event |
| title_full_unstemmed | Insight into data-driven model for the formation mechanism and causative factors of landslides induced by the 2013 Tianshui extreme rainfall event |
| title_short | Insight into data-driven model for the formation mechanism and causative factors of landslides induced by the 2013 Tianshui extreme rainfall event |
| title_sort | insight into data driven model for the formation mechanism and causative factors of landslides induced by the 2013 tianshui extreme rainfall event |
| topic | 2013 Tianshui rainfall event logistic regression (LR) model rainfall-induced landslides susceptibility assessment influencing factors |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2025.2487826 |
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