|本期目录/Table of Contents|

[1]刘帅,王涛*,曾先贵,等.考虑月度降雨变化的多情景区域滑坡易发性评价——以湖南省长沙市为例[J].地球科学与环境学报,2025,47(05):944-961.[doi:10.19814/j.jese.2025.02034]
　LIU Shuai,WANG Tao*,ZENG Xian-gui,et al.Multi-scenario Regional Landslide Susceptibility Assessment Considering Monthly Rainfall Changes—A Case Study of Changsha City, Hunan Province, China[J].Journal of Earth Sciences and Environment,2025,47(05):944-961.[doi:10.19814/j.jese.2025.02034]
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考虑月度降雨变化的多情景区域滑坡易发性评价——以湖南省长沙市为例(PDF)
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《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

卷:
第47卷
期数:
2025年第05期
页码:
944-961
栏目:
工程地质与环境灾害
出版日期:
2025-10-01

文章信息/Info

Title:
Multi-scenario Regional Landslide Susceptibility Assessment Considering Monthly Rainfall Changes—A Case Study of Changsha City, Hunan Province, China
文章编号:
1672-6561(2025)05-0944-18
作者:
刘帅1; 2; 3; 王涛1; 2; 3*; 曾先贵4; 张帅1; 2; 3; 方亚其5; 6
(1. 中国地质科学院地质力学研究所,北京 100081; 2. 自然资源部活动构造与地质安全重点实验室,北京 100081; 3. 自然资源部陕西宝鸡地质灾害野外科学观测研究站,陕西 宝鸡 721001; 4. 长沙市地质灾害监测评价中心,湖南 长沙 410019; 5. 湖南省地质灾害调查监测所,湖南 长沙 410029; 6. 湖南省地质灾害监测预警与应急救援工程技术研究中心,湖南 长沙 410004)
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)
关键词:
降雨;  滑坡;  易发性;  随机森林模型;  皮尔逊相关性;  基尼系数;  主控因子;  湖南
Keywords:
rainfall;  landslide;  susceptibility;  random forest model;  Pearson correlation;  Gini coefficient;  main controlling factor;  Hunan
分类号:
P642.22
DOI:
10.19814/j.jese.2025.02034
文献标志码:
A
摘要:
针对单一情景的滑坡易发性评价难以揭示区域滑坡发育潜势时空变化的问题,在极端降雨事件频发的背景下,以湖南省长沙市为例,融合机器学习算法与空间矩阵动态叠加分析,开展考虑月度降雨变化的滑坡易发性多情景评价研究; 结合皮尔逊(Pearson)相关系数与随机森林基尼(Gini)系数筛选出高程、距河流距离、地形起伏度等15个地质环境因子,构建滑坡易发性影响因子体系; 通过随机森林模型建模,实现基于地质环境因子的静态易发性分区; 基于此,进一步综合多年平均月降水量数据,建立“地质环境本底-动态降雨诱发”双因素叠加模型,揭示了滑坡易发性呈现显著的季节性时空响应特征。结果表明:3月湖南省长沙市东部和西部同步形成滑坡极高易发区; 随着降雨增强,滑坡高易发区自3月至5月呈现向心性扩展态势,其中东部(浏阳市)扩展速率较西部(宁乡市)快20%,形成“东强西弱”的非对称扩展特征; 6月汛期峰值时,宁乡市滑坡极高易发区占比达36.3%,空间分布呈现双核心集聚特征; 7月至8月滑坡易发等级逐步下降; 9月滑坡易发等级骤降,滑坡极高易发区完全消退; 高程(贡献度为19.40%)、坡向(15.54%)、距一级河流的距离(13.35%)及地形起伏度(12.65%)是区域滑坡发育的主控因子,降水量变化显著改变了滑坡易发性的时空分布特征。
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.

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备注/Memo

备注/Memo:
收稿日期:2025-02-27; 修回日期:2025-04-07
基金项目:中国地质调查局地质调查项目(DD20221738,DD20230600204); 国家自然科学基金项目(42302333); 自然资源部科技人才工程项目(121106000000180039-2207); 湖南省地质灾害调查监测所合作科研项目(KJCGZH024)
*通信作者:王 涛(1982-),男,山东枣庄人,研究员,博士研究生导师,工学博士,E-mail:wangtaoig@cags.ac.cn。
通信作者:曾先贵(1980-),男,江西南丰人,工程师,E-mail:21196918@qq.com。
更新日期/Last Update: 2025-10-01