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<p>During 26–28 July 2024, an exceptional rainstorm associated with Typhoon Gaemi triggered widespread clustered landslides in five eastern towns of Zixing County, Hunan Province. However, quantitative analysis of the controlling factors behind such extreme rainstorm-triggered clustered landslides remains insufficient. In this study, a detailed landslide polygon inventory was effectively established using high-resolution satellite imagery and the deep learning algorithm, which was subsequently validated by field investigations. An interpretable machine learning framework, integrating XGBoost algorithm with SHAP (SHapley Additive exPlanations), was developed to quantify the relative contributions of eight critical controlling factors. Spatial analysis reveals that landslides predominantly occurred at elevations of 400–800&#xa0;m, slope gradients of 30–40°, and south-to-southwest-facing slopes. Furthermore, they were closely associated with Caledonian granite strata near fault zones and areas receiving over 500&#xa0;mm of rainfall. The XGBoost model demonstrated robust predictive performance across five-fold cross-validation. SHAP analysis identified 3-day cumulative rainfall as the most significant controlling factor, followed by lithology, distance to faults, and slope aspect. Based on the coupling analysis of these factors, six factor combinations were identified to estimate landslide probability. This multi-method investigation advances our understanding of cascading meteorological-geological hazards and highlights critical controlling factors driving clustered landslide occurrences in subtropical mountainous regions.</p>

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Controlling factors of clustered landslides triggered by extreme rainstorms from the 2024 Typhoon Gaemi in Zixing County, China

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Yeping Zhou,
  • Dalei Peng,
  • Qiang Xu,
  • Chuanhao Pu,
  • Yuyang Shen,
  • Maoyuan Chen,
  • Haoxing Zhao,
  • Ruiyang Liu,
  • Ke Yang

摘要

During 26–28 July 2024, an exceptional rainstorm associated with Typhoon Gaemi triggered widespread clustered landslides in five eastern towns of Zixing County, Hunan Province. However, quantitative analysis of the controlling factors behind such extreme rainstorm-triggered clustered landslides remains insufficient. In this study, a detailed landslide polygon inventory was effectively established using high-resolution satellite imagery and the deep learning algorithm, which was subsequently validated by field investigations. An interpretable machine learning framework, integrating XGBoost algorithm with SHAP (SHapley Additive exPlanations), was developed to quantify the relative contributions of eight critical controlling factors. Spatial analysis reveals that landslides predominantly occurred at elevations of 400–800 m, slope gradients of 30–40°, and south-to-southwest-facing slopes. Furthermore, they were closely associated with Caledonian granite strata near fault zones and areas receiving over 500 mm of rainfall. The XGBoost model demonstrated robust predictive performance across five-fold cross-validation. SHAP analysis identified 3-day cumulative rainfall as the most significant controlling factor, followed by lithology, distance to faults, and slope aspect. Based on the coupling analysis of these factors, six factor combinations were identified to estimate landslide probability. This multi-method investigation advances our understanding of cascading meteorological-geological hazards and highlights critical controlling factors driving clustered landslide occurrences in subtropical mountainous regions.