
浏览全部资源
扫码关注微信
1. 广东电网有限责任公司电力科学研究院广东省电力装备可靠性重点实验室
2. 成都理工大学地质灾害防治与地质环境保护全国重点实验室
3. 应急管理部滑坡灾害风险预警与防控实验室
Published:2026
移动端阅览
魏瑞增, 单云锋, 秦佳松, et al. 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]. Bulletin of Geological Science and Technology, 2026, 45(3): 71-85.
魏瑞增, 单云锋, 秦佳松, et al. 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]. Bulletin of Geological Science and Technology, 2026, 45(3): 71-85. DOI: 10.19509/j.cnki.dzkq.tb20250066.
2024年4月20日,广东省韶关市发生了特大暴雨事件,韶关市江湾镇地区24 h降雨量达到历史极值206 mm,诱发大规模滑坡,导致多地居民房屋遭到损毁、道路中断,引起了社会的广泛关注。及时获取降雨诱发滑坡编目、发育分布规律及主要调控因子对灾后的应急救援决策和恢复重建至关重要。基于Planet高分辨率遥感影像,采用归一化植被指数(normalized difference vegetation index,简称NDVI)差分法自动提取滑坡区域,并绘制滑坡清单。同时,结合地形、降雨和地质环境因素,分析了滑坡分布规律及其成因。此次极端降雨共诱发滑坡1 426处,总面积4.56 km
2
,规模以中小型滑坡为主,主要沿河流呈EN-WS向聚集,形成带状分布,群发性效应显著。空间统计分析显示,滑坡主要集中分布在海拔200~300 m、坡度为20°~30°的斜坡区域。进一步采用逻辑回归、支持向量机、随机森林和极限梯度提升4种机器学习模型,评估降雨诱发滑坡易发性制图精度。结果表明,随机森林和极限梯度提升模型性能最佳,易发区主要分布在河谷两侧的山体斜坡区域。通过SHAP(SHapley additive exPlanations)方法量化分析滑坡的主控因子,发现海拔、降雨量、剖面曲率和地形湿度指数是滑坡发生的关键驱动因素。该研究可为降雨诱发滑坡的快速识别及基于深度学习的易发性评价提供有效方法与数据支持。
李光明,杨玉飞,唐亚明,王小浩,尹春旺,冯凡,周永恒.数据驱动模型评价滑坡易发性的对比研究:以黄河中游流域为例[J].西北地质,2025(02).
袁旭山,刘京会,黄龙生,李鑫旭.基于机器学习模型的新源县滑坡易发性评估[J].科学技术与工程,2025(05).
郑德凤,潘美伊,高敏,闫成林,李媛媛,年廷凯.集中降雨影响下辽南仙人洞国家级自然保护区滑坡灾害多因子风险评价[J].地质科技通报,2025(02).
单云锋,李为乐,周胜森,曲歌,许强.长江上游区域滑坡灾害生态风险评估[J].生态学报,2024(24).
黎子豪,蒋恕.基于机器学习和SHAP算法的声波测井曲线重构及可解释性分析[J].地质科技通报,2025(01).
许强,徐繁树,蒲川豪,李为乐,范宣梅,董秀军,王晓晨,李志刚.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[J].武汉大学学报(信息科学版),2024(08).
杨锟,叶永,谢旋.基于离散元模型的聚合型滑坡碎屑流聚合角度影响研究[J].地质科技通报,2025(02).
王辉,范硕超,黄晓胤,邱鹏,于竞哲,李进田.基于光学影像的滑坡实时自动识别技术研究[J].航天返回与遥感,2024(01).
姚海成,李豹,黄兆棽.基于随机森林算法的电力系统小干扰稳定评估校正[J].广东电力,2023(07).
储栋,管小彬,沈焕锋.考虑全时间序列信息的NDVI变分重建方法[J].地理与地理信息科学,2023(03).
瞿渝,王志辉,于会泳,石娴.基于双时相遥感影像差异信息的深度学习滑坡检测[J].航天返回与遥感,2023(02).
陈晓华,王志平,吴杰康,陈盛语,许海文,孙中海,杨国荣,江剑民,陈锦涛.基于VMD和IAO-SVM的电压暂降源识别方法[J].广东电力,2023(01).
文海家,宋宸昊,向学坤,熊凤光.强降雨诱发滑坡群识别的光学遥感变化检测方法[J].测绘科学,2022(05).
文刚,高孟平,高振宇,周仿荣,潘浩,李志栋,张涛,罗继强,刘星星.多因子协同作用下的云南省山火风险评估[J].广东电力,2022(04).
刘伟旭,吴亮.武江区滑坡和崩塌地质灾害形成条件浅析[J].冶金与材料,2022(01).
吴洁玲,查轩,陈世发,毛兰花,黄博文.1951-2018年韶关不同量级降雨侵蚀力变化[J].水土保持学报,2021(04).
孙昆襄,丁雨淋,曾浩炜,谢潇,朱庆.基于精细DEM的同震滑坡隐患自动提取方法[J].测绘地理信息,2022(04).
李晨阳,王新春,何春珍,吴轩,孔昭煜,李晓蕾.全国1∶200000数字地质图(公开版)空间数据库[J].中国地质,2019(S1).
杨明生,李山山,冯钟葵.结合纹理特征分析与比辐射率估计的震后滑坡提取[J].遥感学报,2018(S1).
杨文涛,汪明,史培军.利用NDVI时间序列识别汶川地震滑坡的分布[J].遥感信息,2012(06).
Chengqiu Li,Jing Zhang,Lichan Gao,Xiaoli Chen,Siyuan Ma,Renmao Yuan.Spatial Clustering and distribution characteristics of large landslides in the Yalong River Basin, China[J].Geomorphology,2025.
Hao Ma,Fawu Wang,Zijin Fu,Youqian Feng,Qi You,Sen Li.Characterizing the clustered landslides triggered by extreme rainfall during the 2024 typhoon Gaemi in Zixing City, Hunan Province, China[J].Landslides,2025.
Hui Shang,Lixiang Su,Yang Liu,Paraskevas Tsangaratos,Ioanna Ilia,Wei Chen,Shaobo Cui,Zhao Duan.Assessment of the effects of characterization methods selection on the landslide susceptibility: a comparison between logistic regression (LR), naive bayes (NB) and radial basis function network (RBF Network)[J].Bulletin of Engineering Geology and the Environment,2025.
Genlan Yang,Longhui Zhao,Yigen Qin,Tao Yang,Shuai Chen.Clustered landslides induced by rainfall in Jiangwan Town, Shaoguan City, Guangdong Province, China[J].Landslides,2025.
Haijun Qiu,Lingling Su,Bingzhe Tang,Dongdong Yang,Mohib Ullah,Yaru Zhu,Ulrich Kamp.The effect of location and geometric properties of landslides caused by rainstorms and earthquakes[J].Earth Surface Processes and Landforms,2024.
Yunfeng Shan,Zhou Xu,Shengsen Zhou,Huiyan Lu,Wenlong Yu,Zhigang Li,Xiong Cao,Pengfei Li,Weile Li.Landslide Hazard Assessment Combined with InSAR Deformation: A Case Study in the Zagunao River Basin, Sichuan Province, Southwestern China[J].Remote Sensing,2023.
Sun Deliang,Gu Qingyu,Wen Haijia,Xu Jiahui,Zhang Yalan,Shi Shuxian,Xue Mengmeng,Zhou Xinzhi.Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization[J].Gondwana Research,2023.
Zhang Junyi,Ma Xianglong,Zhang Jialan,Sun Deliang,Zhou Xinzhi,Mi Changlin,Wen Haijia.Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model.[J].Journal of environmental management,2023.
Zhang Wengang,Gu Xin,Tang Libin,Yin Yueping,Liu Dongsheng,Zhang Yanmei.Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge[J].Gondwana Research,2022.
Shan Yunfeng,Dai Xiaoai,Li Weile,Yang Zhichong,Wang Youlin,Qu Ge,Liu Wenxin,Ren Jiashun,Li Cheng,Liang Shuneng,Zeng Binyang.Detecting Spatial-Temporal Changes of Urban Environment Quality by Remote Sensing-Based Ecological Indices: A Case Study in Panzhihua City, Sichuan Province, China[J].Remote Sensing,2022.
Selamat Siti Norsakinah,Majid Nuriah Abd,Taha Mohd Raihan,Osman Ashraf.Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia[J].Land,2022.
Hakim Wahyu Luqmanul,Rezaie Fatemeh,Nur Arip Syaripudin,Panahi Mahdi,Khosravi Khabat,Lee Chang-Wook,Lee Saro.Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea[J].Journal of Environmental Management,2022.
Kavzoglu, Taskin,Teke, Alihan.Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)[J].Arabian Journal for Science and Engineering,2022(prep).
Wang Haojie,Zhang Limin,Luo Hongyu,He Jian,Cheung R.W.M..AI-powered Landslide Susceptibility Assessment in Hong Kong[J].Engineering Geology,2021.
Goyes Peñafiel Paul,Hernandez Rojas Alejandra.Landslide susceptibility index based on the integration of logistic regression and weights of evidence: A case study in Popayan, Colombia[J].Engineering Geology,2020.
Deliang Sun,Haijia Wen,Danzhou Wang,Jiahui Xu.A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm[J].Geomorphology,2020.
Massimiliano Alvioli,Fausto Guzzetti,Ivan Marchesini.Parameter-free delineation of slope units and terrain subdivision of Italy[J].Geomorphology,2020.
School of Civil Engineering and Architecture, Nanchang University, 330000, Nanchang, China,School of Information Engineering, Nanchang University, 330000, Nanchang, China,School of Civil Engineering and Architecture, Nanchang University, 330000, Nanchang, China,School of Information Engineering, Nanchang University, 330000, Nanchang, China,ARC Centre of Excellence for Geotechnical Science and Engineering, University of Newcastle, Newcastle, NSW, Australia,School of Information Engineering, Nanchang University, 330000, Nanchang, China.A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction[J].Landslides: Journal of the International Consortium on Landslides,2020.
Binh Thai Pham,Trung Nguyen-Thoi,Chongchong Qi,Tran Van Phong,Jie Dou,Lanh Si Ho,Hiep Van Le,Indra Prakash.Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping[J].Catena,2020.
Chao Zhou,Kunlong Yin,Ying Cao,Bayes Ahmed,Yuanyao Li,Filippo Catani,Hamid Reza Pourghasemi.Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China[J].Computers and Geosciences,2018.
Tianqi Chen,Carlos Guestrin.XGBoost: A Scalable Tree Boosting System.[J].CoRR,2016.
Robert Behling,Sigrid Roessner,Hermann Kaufmann,Birgit Kleinschmit.Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data[J].Remote Sensing,2014.
Saro Lee,Joo-Hyung Ryu,Ii-Soo Kim.Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea[J].Landslides,2007.
0
Views
423
下载量
1
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621