2024年4月广东韶关江湾镇极端降雨诱发群发性滑坡初步分析

Preliminary Analysis of Extreme Rainfall-Induced Cluster Landslides in Jiangwan Township,Shaoguan,Guangdong,April 2024

2024年4月中下旬,广东省韶关市发生极端强降雨事件,在韶关江湾镇地区诱发大量滑坡灾害,造成部分地区持续断联近36 h,引起了社会的广泛关注。快速准确地查明滑坡基本特征、发育分布规律及形成条件对于灾害应急决策和风险隐患排除处置至关重要。利用灾后的光学遥感影像并结合深度学习模型,对韶关江湾镇降雨诱发滑坡进行了快速智能识别与人工校核,共解译出1192处滑坡,总面积约3.14 km^(2)。滑坡规模以中小型滑坡为主,主要沿河流呈北东-南西向聚集带状分布,群发性效应显著。空间统计分析表明,滑坡主要分布在200~300 m高程范围内、坡度为10°~30°的凹坡上。进一步使用随机森林模型与SHAP理论对滑坡的地貌主控因子进行量化分析,发现不同地形地貌因子对滑坡形成均有不同程度的非线性影响,高程、坡度和汇水条件等多因素耦合作用共同控制了滑坡的形成。该研究突出了基于深度学习的智能识别与分析技术在滑坡灾害应急调查与形成条件分析中的巨大优势,可为灾害损失快速评估和风险隐患排查提供重要技术支撑。

Objectives:In mid-to late-April 2024,an extreme heavy rainfall event occurred in Shaoguan City,Guangdong Province,inducing a large number of landslides in Jiangwan Town,Shaoguan.People lost connection with the outside world for nearly 36 hours,which aroused widespread social concern.Rapidly and accurately identifying the basic characteristics of landslides,development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal.Methods:Using the post-disaster optical remote sensing images and combining with deep learning model,the rainfall-induced landslides in Jiangwan Town,Shaoguan,were quickly and automatically identified.Results:After manually calibration,a total of 1192 landslides were deciphered,with a total area of about 3.14 km².The scale of the landslides was dominated by small and medium-sized landslides,which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction,with a significant characteristic of concentrated occurrence.Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30°in the range of 200-300 m elevation.Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation,and that multiple factors such as elevation,slope,and catchment conditions are coupled to jointly control the formation of landslides.Conclusions:This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters,which can provide important technical support for the rapid assessment of disaster losses and risk identification.