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Multi-scenario Regional Landslide Susceptibility Assessment Considering Monthly Rainfall Changes—A Case Study of Changsha City, Hunan Province, China(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2025年第05期
Page:
944-961
Research Field:
工程地质与环境灾害
Publishing date:

Info

Title:
Multi-scenario Regional Landslide Susceptibility Assessment Considering Monthly Rainfall Changes—A Case Study of Changsha City, Hunan Province, China
Author(s):
LIU Shuai1; 2; 3;  WANG Tao1; 2; 3*;  ZENG Xian-gui4;  ZHANG Shuai1; 2; 3;  FANG Ya-qi5; 6
(1. Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China; 2. Key Laboratory of Active Tectonics and Geological Safety, Ministry of Natural Resources, Beijing 100081, China; 3. Observation and Research Station of Geological Disaster in Baoji, Shaanxi Province, Ministry of Natural Resources, Baoji 721001, Shaanxi, China; 4. Changsha Geological Hazard Monitoring and Evaluation Center, Changsha 410019, Hunan, China; 5. Geohazards Survey and Monitor Institute of Hunan Province, Changsha 410029, Hunan, China; 6. Hunan Geological Disaster Monitoring, Early Warning and Emergency Rescue Engineering Technology Research Center, Changsha 410004, Hunan, China)
Keywords:
rainfall;  landslide;  susceptibility;  random forest model;  Pearson correlation;  Gini coefficient;  main controlling factor;  Hunan
PACS:
P642.22
DOI:
10.19814/j.jese.2025.02034
Abstract:
To address the limitation of single-scenario landslide susceptibility assessment in capturing the spatiotemporal variations of regional landslide development potential, a multi-scenario evaluation considering monthly rainfall changes in Changsha city of Hunan province was conducted by integrating machine learning algorithms and spatial matrix dynamic superposition analysis under the background of frequent extreme rainfall events; fifteen geoenvironmental factors, including elevation, distance to rivers and topographic relief, were selected by Pearson correlation and random forest Gini coefficients to construct a landslide susceptibility factor system; random forest(RF)model was applied to achieve static susceptibility zoning based on geoenvironmental factors; further, by incorporating multi-year average monthly rainfall data, a geoenvironmental background-dynamic rainfall triggering two-factor superposition model was established, revealing significant seasonal spatiotemporal response characteristics of landslide susceptibility. The results show that ① extremely high susceptibility zones of landslide form simultaneously in the eastern and western of Changsha city, Hunan province in March; ② with the increase of rainfall, the high susceptibility zones expand concentrically from March to May, with the east(Liuyang city)expanding 20% faster than the west(Ningxiang city), forming an asymmetric “east strong, west weak” expansion pattern; ③ by June during the peak flood season, Ningxiang city's extremely high susceptibility zones account for 36.3%, showing a dual-core agglomeration in spatial distribution; ④ susceptibility levels gradually decline from July to August and drop sharply in September, with extremely high susceptibility zones completely disappearing; ⑤ elevation(the contribution degree is 19.40%), slope aspect(15.54%), distance to primary rivers(13.35%), and topographic relief(12.65%)are identified as the main controlling factors of regional landslide development, while rainfall changes significantly alter the spatiotemporal distribution of landslide susceptibility.

References:

[1] ALVIOLI M,MELILLO M,GUZZETTI F,et al.Implications of Climate Change on Landslide Hazard in Central Italy[J].Science of the Total Environment,2018,630:1528-1543.
[2] 殷跃平,高少华.高位远程地质灾害研究:回顾与展望[J].中国地质灾害与防治学报,2018,29(5):1-21.
YIN Yue-ping,GAO Shao-hua.Research on High-altitude and Long-runout Rockslides:Review and Pro-spects[J].The Chinese Journal of Geological Hazard and Control,2018,29(5):1-21.
[3] SEGONI S,NOCENTINI N,BARBADORI F,et al.A Novel Prototype National-scale Landslide Nowcasting System for Italy Combining Rainfall Thresholds and Risk Indicators[J].Landslides,2025,22(5):1341-1366.
[4] 陈 贺,付有旺.贡山县进藏通道群发性滑坡地质特征与降雨阈值[J].岩石力学与工程学报,2024,43(增2):3861-3871.
CHEN He,FU You-wang.Geological Characteristics and Rainfall Threshold of Multiple Landslides in the Passage into Tibet in Gongshan County[J].Chinese Journal of Rock Mechanics and Engineering,2024,43(S2):3861-3871.
[5] 易靖松,王 峰,程英建,等.高山峡谷区地质灾害危险性评价:以四川省阿坝县为例[J].中国地质灾害与防治学报,2022,33(3):134-142.
YI Jing-song,WANG Feng,CHENG Ying-jian,et al.Study on the Risk Assessment of Geological Disasters in Alpine Valley Area:A Case Study in Aba County,Sichuan Province[J].The Chinese Journal of Geologi-cal Hazard and Control,2022,33(3):134-142.
[6] 李 萍,李丽慧,刘昊磾,等.澜沧江流域重大水电工程扰动灾害风险评价[J].工程地质学报,2022,30(3):635-647.
LI Ping,LI Li-hui,LIU Hao-di,et al.Risk Assessment of Disturbance Disaster in Major Hydropower Engineering of Lancang River Basin[J].Journal of Engineering Geology,2022,30(3):635-647.
[7] 刘 帅,朱杰勇,杨得虎,等.不同降雨工况条件下的崩滑地质灾害危险性评价[J].地质科技通报,2024,43(2):253-267.
LIU Shuai,ZHU Jie-yong,YANG De-hu,et al.Geological Hazard Risk Assessment of Collapse and Landslide Under Different Rainfall Conditions[J].Bu-lletin of Geological Science and Technology,2024,43(2):253-267.
[8] 舒和平.融合降雨阈值和易发性的黄土滑坡预警研究[C]∥青年地学论坛理事会.第九届青年地学论坛论文集.兰州:青年地学论坛理事会,2024:358-359.
SHU He-ping.Research on Early Warning of Loess Landslides by Integrating Rainfall Threshold and Susceptibility[C]∥Council of Young Geoscience Forum.Proceedings of the 9th Young Geoscience Forum.Lanzhou:Council of Young Geoscience Forum,2024:358-359.
[9] WANG X M,DIAO M P,GUO H N,et al.Landslide Deformation Prediction and Automatic Warning by Coupling Machine Learning and Physical Models[J].Earth and Space Science,2024,11(6):e2023EA003238.
[10] LIN G,LAN H X,LI P,et al.Role of Rainfall Temporal Distribution on Effective Infiltration in the Loess Slope and Prediction Model[J].Engineering Geo-logy,2024,335:107541.
[11] CHENG Y X,GAN Y,SHI C M,et al.A Critical Analysis of Geological Hazard Risk Assessment Including Future Perspectives[J].Sustainability,2024,16(9):3701.
[12] LI L P,LAN H X,GUO C B,et al.A Modified Frequency Ratio Method for Landslide Susceptibility Assessment[J].Landslides,2017,14(2):727-741.
[13] YAO X,THAM L G,DAI F C.Landslide Susceptibi-lity Mapping Based on Support Vector Machine:A Case Study on Natural Slopes of Hong Kong,China[J].Geomorphology,2008,101(4):572-582.
[14] DENG H,WU X T,ZHANG W J,et al.Slope-unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas[J].Remote Sensing,2022,14(17):4245.
[15] 吴润泽,胡旭东,梅红波,等.基于随机森林的滑坡空间易发性评价:以三峡库区湖北段为例[J].地球科学,2021,46(1):321-330.
WU Run-ze,HU Xu-dong,MEI Hong-bo,et al.Spatial Susceptibility Assessment of Landslides Based on Random Forest:A Case Study from Hubei Section in the Three Gorges Reservoir Area[J].Earth Science,2021,46(1):321-330.
[16] 刘 帅,王 涛,曹佳文,等.基于优化随机森林模型的降雨群发滑坡易发性评价:以西秦岭极端降雨事件为例[J].地质通报,2024,43(6):958-970.
LIU Shuai,WANG Tao,CAO Jia-wen,et al.Susceptibility Assessment of Precipitation-induced Mass Landslides Based on an Optimal Random Forest Mo-del:Taking the Extreme Precipitation Event in the Western Qinling Mountains as an Example[J].Geological Bulletin of China,2024,43(6):958-970.
[17] PRADHAN B.A Comparative Study on the Predictive Ability of the Decision Tree,Support Vector Machine and Neuro-fuzzy Models in Landslide Susceptibility Mapping Using GIS[J].Computers & Geosciences,2013,51:350-365.
[18] ERMINI L,CATANI F,CASAGLI N.Artificial Neural Networks Applied to Landslide Susceptibility Assessment[J].Geomorphology,2005,66(1/2/3/4):327-343.
[19] HAKIM W L,REZAIE F,NUR A S,et al.Convolutional Neural Network(CNN)with Metaheuristic Optimization Algorithms for Landslide Susceptibility Mapping in Icheon,South Korea[J].Journal of Environmental Management,2022,305:114367.
[20] DOU J,YUNUS A P,TIENBUI D,et al.Assessment of Advanced Random Forest and Decision Tree Algorithms for Modeling Rainfall-induced Landslide Susceptibility in the Izu-Oshima Volcanic Island,Japan[J].Science of the Total Environment,2019,662:332-346.
[21] MERGHADI A,YUNUS A P,DOU J,et al.Machine Learning Methods for Landslide Susceptibility Stu-dies:A Comparative Overview of Algorithm Perfor-mance[J].Earth-science Reviews,2020,207:103225.
[22] 楼鲜荭,孔存书,杨世勇,等.粉砂岩制砂取代河砂对砼强度的影响研究及意义:浏阳高坪沿甸村坳上组滑坡地质灾害治理工程实践[J].价值工程,2020,39(27):126-128.
LOU Xian-hong,KONG Cun-shu,YANG Shi-yong,et al.Research and Significance of Replacing River Sand with Silt Sand on Concrete Strength:The Engineering Practice of Landslide Geological Hazard Treatment in the Aoshang Group of Yandian Village,Gaoping,Liuyang[J].Value Engineering,2020,39(27):126-128.
[23] 长沙市自然资源和规划局.关于加强我市山体滑坡防治工作的建议答复[R].长沙:长沙市自然资源和规划局,2023.
Changsha Municipal Bureau of Natural Resources and Planning.Suggestions and Response to Strengthening the Prevention and Control of Landslides in Our City[R].Changsha:Changsha Municipal Bureau of Natural Resources and Planning,2023.
[24] YANG X,JIANG Y.Experimental Study on the Mechanical Properties of Weathered Granite Under Different Water Contents[J].Rock Mechanics and Rock Engineering,2018,51(10):3339-3351.
[25] AHMAD A,FARIDA M,JUITA N,et al.Soil Micromorphology for Modeling Spatial on Landslide Susceptibility Mapping:A Case Study in Kelara Subwatershed,Jeneponto Regency of South Sulawesi,Indonesia[J].Natural Hazards,2023,118(2):1445-1462.
[26] REICHENBACH P,ROSSI M,MALAMUD B D,et al.A Review of Statistically-based Landslide Susceptibi-lity Models[J].Earth-science Reviews,2018,180:60-91.
[27] ROSSI M,REICHENBACH P.LAND-SE:A Softwa-re for Statistically Based Landslide Susceptibility Zonation,Version 1.0[J].Geoscientific Model Development,2016,9(10):3533-3543.
[28] 唐辉明,鲁 莎.三峡库区黄土坡滑坡滑带空间分布特征研究[J].工程地质学报,2018,26(1):129-136.
TANG Hui-ming,LU Sha.Research on the Spatial Distribution Characteristics of Slip Zone of the Huangtupo Landslide in Three Gorges Reservoir Area[J].Journal of Engineering Geology,2018,26(1):129-136.
[29] DOU H Q,HE J B,HUANG S Y,et al.Influences of Non-landslide Sample Selection Strategies on Landsli-de Susceptibility Mapping by Machine Learning[J].Geomatics,Natural Hazards and Risk,2023,14(1):2285719.
[30] WANG Z P,CHEN J P,LIAN Z C,et al.Influence of Buffer Distance on Environmental Geological Hazard Susceptibility Assessment[J].Environmental Science and Pollution Research,2024,31(6):9582-9595.
[31] CHEN W,CHEN X,PENG J B,et al.Landslide Susceptibility Modeling Based on ANFIS with Teaching-learning-based Optimization and Satin Bowerbird Optimizer[J].Geoscience Frontiers,2021,12(1):93-107.
[32] BUI D T,TSANGARATOS P,NGUYEN V T,et al.Comparing the Prediction Performance of a Deep Lea-rning Neural Network Model with Conventional Machine Learning Models in Landslide Susceptibility Assessment[J].Catena,2020,188:104426.
[33] HUANG F M,CAO Z S,JIANG S H,et al.Landslide Susceptibility Prediction Based on a Semi-supervised Multiple-layer Perceptron Model[J].Landslides,2020,17(12):2919-2930.
[34] YILMAZ I.Landslide Susceptibility Mapping Using Frequency Ratio,Logistic Regression,Artificial Neural Networks and Their Comparison:A Case Study from Kat Landslides(Tokat:Turkey)[J].Computers & Geosciences,2009,35(6):1125-1138.
[35] YOUSSEF A M,POURGHASEMI H R,POURTAGHI Z S,et al.Landslide Susceptibility Mapping Using Random Forest,Boosted Regression Tree,Classification and Regression Tree,and General Linear Models and Comparison of Their Performance at Wadi Tay-yah Basin,Asir Region,Saudi Arabia[J].Landslides,2016,13(5):839-856.
[36] SHAREK D,MCCANN E,HUNTLEY-MOORE S.A Mixed-methods Evaluation of a Gender Affirmative Education Program for Families of Trans Young People[J].Journal of GLBT Family Studies,2020,16(1):18-31.
[37] FAN W,WEI X S,CAO Y B,et al.Landslide Susceptibility Assessment Using the Certainty Factor and Analytic Hierarchy Process[J].Journal of Mountain Science,2017,14(5):906-925.
[38] CANTARINO I,CARRION M A,GOERLICH F,et al.A ROC Analysis-based Classification Method for Landslide Susceptibility Maps[J].Landslides,2019,16(2):265-282.
[39] 黄发明,叶 舟,姚 池,等.滑坡易发性预测不确定性:环境因子不同属性区间划分和不同数据驱动模型的影响[J].地球科学,2020,45(12):4535-4549.
HUANG Fa-ming,YE Zhou,YAO Chi,et al.Uncertainties of Landslide Susceptibility Prediction:Diffe-rent Attribute Interval Divisions of Environmental Factors and Different Data-based Models[J].Earth Science,2020,45(12):4535-4549.
[40] GB/T 28592—2012,降水量等级[S].
GB/T 28592—2012,Grade of Precipitation[S].
[41] 黄发明,石 雨,欧阳慰平,等.基于证据权和卡方自动交互检测决策树的滑坡易发性预测[J].土木与环境工程学报,2022,44(5):43-51.
HUANG Fa-ming,SHI Yu,OUYANG Wei-ping,et al.Landslide Susceptibility Prediction Modeling Ba-sed on Weight of Evidence and Chi-square Automatic Interactive Detection Decision Tree[J].Journal of Ci-vil and Environmental Engineering,2022,44(5):43-51.
[42] SUN D L,WANG J,WEN H J,et al.Insights into Landslide Susceptibility in Different Karst Erosion Landforms Based on Interpretable Machine Learning[J].Earth Surface Processes and Landforms,2024,49(3):1006-1027.
[43] HUANG F M,XIONG H W,JIANG S H,et al.Mo-delling Landslide Susceptibility Prediction:A Review and Construction of Semi-supervised Imbalanced Theory[J].Earth-science Reviews,2024,250:104700.
[44] 长沙市应急管理局.望城区乌山街道黄花岭村山体滑坡应急处置情况通报[R].长沙:长沙市应急管理局,2024.
Department of Emergency Management of Changsha City.Bulletin on the Emergency Handling of the Landslide in Huanghualing Village,Wushan Sub-district,Wangcheng District[R].Changsha:Department of Emergency Management of Changsha City,2024.
[45] 浏阳市自然资源局.大瑶镇李畋村常春小区后山坍塌地质灾害调查报告[R].浏阳:浏阳市自然资源局,2020.
Liuyang Bureau of Natural Resources.Investigation Report on the Geological Disaster of the Collapse of the Rear Mountain of Changchun Community,Litian Village,Dayao Town[R].Liuyang:Liuyang Bureau of Natural Resources,2020.

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Last Update: 2025-10-01