地质科技通报2026，Vol.45Issue(3)：71-85,15.DOI:10.19509/j.cnki.dzkq.tb20250066
广东省韶关市"4·20"极端降雨诱发滑坡发育特征及其主控因子分析
Development characteristics and controlling factors of landslides triggered by extreme rainfall on April 20,2024 in Shaoguan City,Guangdong Province
摘要
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.关键词
极端降雨/群发性滑坡/智能提取/分布规律/主控因子/机器学习/广东省韶关市Key words
extreme rainfall/group-occurring landslide/intelligent extraction/distribution pattern/controlling factor/machine learning/Shaoguan City,Guangdong Province分类
天文与地球科学引用本文复制引用
魏瑞增,单云锋,秦佳松,王磊,彭大伟,何国庆,范禄震,李为乐..广东省韶关市"4·20"极端降雨诱发滑坡发育特征及其主控因子分析[J].地质科技通报,2026,45(3):71-85,15.基金项目
中国南方电网有限责任公司科技项目(GDKJXM20230770) （GDKJXM20230770）
四川省重点研发项目(2023YFS0435) （2023YFS0435）
地质灾害防治与地质环境保护国家重点实验室自主研究课题(SKLGP2022Z007) （SKLGP2022Z007）
