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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| Format: | Article |
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Editorial Office of Pearl River
2025-06-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2025.06.006 |
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| author | JIN Yi YU Haixia |
| author_facet | JIN Yi YU Haixia |
| author_sort | JIN Yi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-583e44e9d64246fcbdcb689c9dae0ef5 |
| institution | DOAJ |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-583e44e9d64246fcbdcb689c9dae0ef52025-08-20T03:15:20ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352025-06-01465259113124860Landslide Hazard Warning in Guangdong-Hong Kong-Macao Greater Bay Area Based on Historical Ranking Rainfall ThresholdJIN YiYU HaixiaThis 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.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2025.06.006semi-supervised machine learningnon-landslide samplerainfall thresholdlandslide hazardGuangdong-Hong Kong-Macao Greater Bay Area |
| spellingShingle | JIN Yi YU Haixia Landslide Hazard Warning in Guangdong-Hong Kong-Macao Greater Bay Area Based on Historical Ranking Rainfall Threshold Renmin Zhujiang semi-supervised machine learning non-landslide sample rainfall threshold landslide hazard Guangdong-Hong Kong-Macao Greater Bay Area |
| title | Landslide Hazard Warning in Guangdong-Hong Kong-Macao Greater Bay Area Based on Historical Ranking Rainfall Threshold |
| title_full | Landslide Hazard Warning in Guangdong-Hong Kong-Macao Greater Bay Area Based on Historical Ranking Rainfall Threshold |
| title_fullStr | Landslide Hazard Warning in Guangdong-Hong Kong-Macao Greater Bay Area Based on Historical Ranking Rainfall Threshold |
| title_full_unstemmed | Landslide Hazard Warning in Guangdong-Hong Kong-Macao Greater Bay Area Based on Historical Ranking Rainfall Threshold |
| title_short | Landslide Hazard Warning in Guangdong-Hong Kong-Macao Greater Bay Area Based on Historical Ranking Rainfall Threshold |
| title_sort | landslide hazard warning in guangdong hong kong macao greater bay area based on historical ranking rainfall threshold |
| topic | semi-supervised machine learning non-landslide sample rainfall threshold landslide hazard Guangdong-Hong Kong-Macao Greater Bay Area |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2025.06.006 |
| work_keys_str_mv | AT jinyi landslidehazardwarninginguangdonghongkongmacaogreaterbayareabasedonhistoricalrankingrainfallthreshold AT yuhaixia landslidehazardwarninginguangdonghongkongmacaogreaterbayareabasedonhistoricalrankingrainfallthreshold |