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WEI Ruizeng，SHAN Yunfeng，QIN Jiasong，et al. Development characteristics and controlling factors of landslides triggered by extreme rainfall on April 20, 2024, in Shaoguan City, Guangdong Province[J]. Bulletin of Geological Science and Technology，2026，45（3）：1-15 doi: 10.19509/j.cnki.dzkq.tb20250066
Citation: WEI Ruizeng，SHAN Yunfeng，QIN Jiasong，et al. Development characteristics and controlling factors of landslides triggered by extreme rainfall on April 20, 2024, in Shaoguan City, Guangdong Province[J]. Bulletin of Geological Science and Technology，2026，45（3）：1-15 doi: 10.19509/j.cnki.dzkq.tb20250066
shu

Development characteristics and controlling factors of landslides triggered by extreme rainfall on April 20, 2024, in Shaoguan City, Guangdong Province

doi: 10.19509/j.cnki.dzkq.tb20250066
  • 1.

    Guangdong Key Laboratory of Electric Power Equipment Reliability, Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China

  • 2.

    State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China

  • 3.

    Key Laboratory of Early Warning and Prevention of Landslide Disaster Risk of Ministry of Emergency Management, Chengdu 610059, China

More Information
    Abstract
  • Objective 

    On April 20, 2024, an extreme rainstorm event occurred in Shaoguan City, Guangdong Province, South China. The 24-hour rainfall in Jiangwan Town reached a historical maximum value of 206 mm, which triggered a large number of landslides. These hazards caused serious damage to residential buildings, road blockages, and widespread social concern. Timely acquisition of landslide inventories, understanding their development distribution patterns, and identifying main controlling factors are crucial for post-disaster emergency response and reconstruction.

    Methods 

    Based on high-resolution Planet remote sensing images, the normalized difference vegetation index (NDVI) difference method combined with terrain correction and morphological post-processing was adopted to automatically extract landslide areas. A complete landslide inventory was compiled. Meanwhile, the spatial distribution patterns and causal factors of the landslides were analyzed, combined with topographic, rainfall, and geological environmental factors. The SHapley additive exPlanations (SHAP) method was applied to quantitatively identify the dominant controlling factors of landslide occurrence.

    Results 

    The results showed that the extreme rainfall event triggered 1 426 landslides in total, with a total area of 4.56 km2, mainly small to medium scale in size. Landslides predominantly clustered along rivers in a Northeast-Southwest orientation, forming belt-like distributions, with notable group-occurring effects. Spatial statistical analysis revealed that landslides were intensively distributed in slope areas with elevations of 200-300 m and slopes of 20°-30°. Four machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost), were used to evaluate the accuracy of landslide susceptibility mapping. The results showed that random forest and eXtreme gradient boosting models performed best, identifying highly susceptible areas mainly on mountain slopes on both sides of the river valleys. Through quantitative analysis of the main controlling factors of landslides using the SHAP method, it was found that elevation, rainfall, profile curvature, and topographic wetness index (TWI) were the key driving factors for landslide occurrence.

    Conclusion 

    This study provides reliable technical approaches, refined data support, and practical reference for rapid identification of rainfall-induced group-occurring landslides and machine learning-based susceptibility evaluation in similar mountainous areas.

     

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