Landslide Hazard Warning in Guangdong-Hong Kong-Macao Greater Bay Area Based on Historical Ranking Rainfall Threshold
This study focused on the Guangdong-Hong Kong-Macao Greater Bay Area and constructed a grid-based landslide hazard assessment model to enhance regional disaster prevention and mitigation capabilities. A semi-supervised learning method was used to optimize the proportional selection of landslide poin...
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| Main Authors: | , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Editorial Office of Pearl River
2025-06-01
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| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2025.06.006 |
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| Summary: | This study focused on the Guangdong-Hong Kong-Macao Greater Bay Area and constructed a grid-based landslide hazard assessment model to enhance regional disaster prevention and mitigation capabilities. A semi-supervised learning method was used to optimize the proportional selection of landslide points and non-landslide points to reduce the uncertainty of susceptibility modeling. A historical ranking rainfall threshold-based method was proposed to classify daily rainfall, 3-day cumulative rainfall, and 7-day cumulative rainfall data. The spatial susceptibility of landslides and rainfall-induced probability were quantitatively coupled to establish a dynamic landslide hazard warning system. The results indicate that when a 12.5-meter evaluation unit scale is used within the Guangdong-Hong Kong-Macao Greater Bay Area, the optimal ratio of landslide points to non-landslide points is 1:4. Furthermore, the area under curve (AUC) value of the susceptibility model reaches as high as 0.973. In practical application during June 2018, the warning system accurately predicted 25 rainfall-induced landslide events, with 72% occurring in extremely high-risk warning zones and 28% in high-risk warning zones, validating the model's effectiveness. This system achieves fine-scale landslide hazard warnings in the Greater Bay Area, providing scientific support for regional landslide risk management. |
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| ISSN: | 1001-9235 |