基于机器学习的高植被覆盖区降雨滑坡识别

亓莉宁; 郭朝旭; 孟华君; 楚克磊; 王园园

doi:10.19509/j.cnki.dzkq.tb20240298

您当前的位置：

首页 >

文章列表页 >

基于机器学习的高植被覆盖区降雨滑坡识别

工程地质与地质灾害 | 更新时间：2026-11-05

- 基于机器学习的高植被覆盖区降雨滑坡识别
- 基于机器学习的高植被覆盖区降雨滑坡识别
- 地质科技通报 2026年45卷第1期页码：185-198
- 作者机构：
  
  1. 中国地质科学院地质力学研究所
  2. 自然资源部丘陵山地地质灾害防治重点实验室
  3. 福建省地质灾害重点实验室
  4. 中国地质大学(北京)地球科学与资源学院
  5. 自然资源部陕西宝鸡地质灾害野外科学观测研究站
  6. 中国地质科学院
- 作者简介：
- 基金信息：
  
  福建省自然资源科技创新项目(KY-07000-04-2022-023);国家自然科学重点基金项目(42130720);中国地质科学院地质力学研究所项目(所科研87)
- DOI：10.19509/j.cnki.dzkq.tb20240298
  中图分类号： TP181;P642.22
- 纸质出版：2026
- 稿件说明：
移动端阅览
亓莉宁, 郭朝旭, 孟华君, 等. 基于机器学习的高植被覆盖区降雨滑坡识别[J]. 地质科技通报, 2026,45(1):185-198.

亓莉宁, 郭朝旭, 孟华君, et al. 基于机器学习的高植被覆盖区降雨滑坡识别[J]. Bulletin of Geological Science and Technology, 2026, 45(1): 185-198.
亓莉宁, 郭朝旭, 孟华君, 等. 基于机器学习的高植被覆盖区降雨滑坡识别[J]. 地质科技通报, 2026,45(1):185-198. DOI： 10.19509/j.cnki.dzkq.tb20240298.

亓莉宁, 郭朝旭, 孟华君, et al. 基于机器学习的高植被覆盖区降雨滑坡识别[J]. Bulletin of Geological Science and Technology, 2026, 45(1): 185-198. DOI： 10.19509/j.cnki.dzkq.tb20240298.

摘要

近年来，受全球气候变暖影响，我国东南沿海省份频繁遭受超强台风暴雨和梅雨侵袭，诱发了大量群发性浅层滑坡灾害，严重威胁当地人民群众的生命和财产安全。为第一时间获取较准确灾害信息支撑应急救灾科学决策，针对高植被覆盖区强降雨诱发花岗岩强风化层形成的群发性浅层滑坡，采用机器学习方法提出了一种基于决策树轻量级梯度提升机（light gradient boosting machine，简称LightGBM）算法的浅层滑坡自动识别模型，并利用福建省武平县高精度航拍影像和数字正射影像（DOM）数据选取坡度、高程、平面曲率（SOA）、剖面曲率（SOS）和R、G、B三波段等特征参数搭建识别模型进行训练和测试。结果表明：(1)多特征组合模型的滑坡识别准确率优于单特征模型；(2)由坡度、高程、平面曲率、剖面曲率和R、G、B三波段组成的快速识别模型能准确识别已发育滑坡体，准确率（Accuracy）和召回率（Recall）值均大于0.99；(3)去除可视化图层后，采用该模型开展高植被覆盖区滑坡早期识别时准确率（Accuracy）和召回率（Recall）值仍达0.86；推广至武平县全县范围时，准确率（Accuracy）达0.80，表明识别准确度较高；(4)识别模型总体得分（F1score）高达0.86，推广至武平县全县范围时，F1score高达0.84，表明该模型泛化能力较强，能够较准确对相似地质环境孕育的滑坡灾害实现自动识别。研究成果为高植被区降雨型滑坡灾害早期识别提供了一种新的思路，可为海西台风暴雨型滑坡地质灾害风险防控和救灾决策提供依据和科技支撑。

Abstract

关键词

Keywords

references

高瑞源.基于机器学习方法的区域泥石流易发性评价研究[D].吉林大学,2023(06).

李航, 著.统计学习方法[M].清华大学出版社,2012.

李天毅,戴宁,丛佩娟,陈恒昌,贾继宏,李扬,贵欣瑞.“23·7”京津冀地区暴雨洪水与土壤侵蚀调查——以河北省邢台市临城县为例[J].水土保持通报,2024(02).

王灿星,朱杰勇,喻聪骏,刘家恺,祝传兵.基于皮尔逊Ⅲ型曲线的不同降雨工况下的崩滑地质灾害危险性评价[J].地质科技通报,2025(01).

罗常伟,王双双,尹峻松,朱思宇,林波,曹江.集成学习研究现状及展望[J].指挥与控制学报,2023(01).

邬礼扬,殷坤龙,曾韬睿,刘书豪,刘真意.不同栅格尺寸下输电线路地质灾害易发性评价[J].地质科技通报,2024(01).

吴伟伟,胥涛,高志强,刘滨.山西晋中地区“2021.10”暴雨特征与洪灾分析[J].中国防汛抗旱,2022(12).

李星,杨赛,李远耀,殷坤龙,王伟.面向区域滑坡易发性精细化评价的改进斜坡单元法[J].地质科技通报,2023(03).

河南郑州“7·20”特大暴雨灾害调查报告公布[J].中国应急管理,2022(02).

许强,陆会燕,李为乐,董秀军,郭晨.滑坡隐患类型与对应识别方法[J].武汉大学学报(信息科学版),2022(03).

冀全伟,王文磊,刘治博,祝茂强,袁长江.一种基于机器学习算法的岩性填图方法[J].地质力学学报,2021(03).

吴季寰,张春山,孟华君,郭涵,吴坤罡,李洪嘉.抚顺西露天矿区滑坡易发性评价与时空特征分析[J].地质力学学报,2021(03).

Danyang Zhou,Zhen Zhang,Jiachun Li,Xiaoliang Wang.Seepage-stress coupled modeling for rainfall induced loess landslide[J].Theoretical & Applied Mechanics Letters,2019(01).

徐继伟,杨云.集成学习方法:研究综述[J].云南大学学报(自然科学版),2018(06).

Bo Chen,Zhenhong Li,Chuang Song,Chen Yu,Wu Zhu,Zhenjiang Liu,Bingquan Han,Jiantao Du,Chenglong Zhang,Fu Xu,Jianbing Peng.Automatic detection of active geohazards with millimeter-to-meter-scale deformation and quantitative analysis of factors influencing spatial distribution: A case study in the Hexi corridor, China[J].International Journal of Applied Earth Observation and Geoinformation,2024.

Niu Chong,Yin Wenping,Xue Wei,Sui Yujing,Xun Xingqing,Zhou Xiran,Zhang Sheng,Xue Yong.Multi-Window Identification of Landslide Hazards Based on InSAR Technology and Factors Predisposing to Disasters[J].Land,2023.

Liu Yanhui,Huang Junbao,Xiao Ruihua,Ma Shiwei,Zhou Pinggen.Research on a Regional Landslide Early-Warning Model Based on Machine Learning—A Case Study of Fujian Province, China[J].Forests,2022.

Pang Dongdong,Liu Gang,He Jing,Li Weile,Fu Rao.Automatic Remote Sensing Identification of Co-Seismic Landslides Using Deep Learning Methods[J].Forests,2022.

van Natijne A.L.,Bogaard T.A.,van Leijen F.J.,Hanssen R.F.,Lindenbergh R.C..World-wide InSAR sensitivity index for landslide deformation tracking[J].International Journal of Applied Earth Observation and Geoinformation,2022.

Liang Jingtao,Dong Jihong,Zhang Su,Zhao Cong,Liu Bin,Yang Lei,Yan Shengwu,Ma Xiaobo.Discussion on InSAR Identification Effectivity of Potential Landslides and Factors That Influence the Effectivity[J].Remote Sensing,2022(8).

Ren Tianhe,Gong Wenping,Gao Liang,Zhao Fumeng,Cheng Zhan.An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification[J].Remote Sensing,2022(5).

Li Genger,Hu Bo,Li Hui,Lu Feng.Early Identifying and Monitoring Landslides in Guizhou Province with InSAR and Optical Remote Sensing[J].Journal of Sensors,2021.

Yian Wang,Donglie Liu,Jie Dong,Lu Zhang,Jincheng Guo,Mingsheng Liao,Jianya Gong.On the applicability of satellite SAR interferometry to landslide hazards detection in hilly areas: a case study of Shuicheng, Guizhou in Southwest China[J].Landslides,2021.

Viet-Ha Nhu,Ayub Mohammadi,Himan Shahabi,Baharin Bin Ahmad,Nadhir Al-Ansari,Ataollah Shirzadi,Marten Geertsema,Victoria R. Kress,Sadra Karimzadeh,Khalil Valizadeh Kamran,Wei Chen,Hoang Nguyen.Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms[J].Forests,2020.

Dieu Tien Bui,Paraskevas Tsangaratos,Viet-Tien Nguyen,Ngo Van Liem,Phan Trong Trinh.Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment[J].Catena,2020.

Maher Ibrahim Sameen,Biswajeet Pradhan,Saro Lee.Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment[J].Catena,2020.

Jie Dou,Ali P. Yunus,Dieu Tien Bui,Abdelaziz Merghadi,Mehebub Sahana,Zhongfan Zhu,Chi-Wen Chen,Zheng Han,Binh Thai Pham.Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan[J].Landslides,2020(3).

Wang Yu,Wang Xiaofei,Jian Junfan.Remote Sensing Landslide Recognition Based on Convolutional Neural Network[J].Mathematical Problems in Engineering,2019.

Omid Ghorbanzadeh,Thomas Blaschke,Khalil Gholamnia,Sansar Raj Meena,Dirk Tiede,Jagannath Aryal.Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection[J].Remote Sensing,2019.

original> .A practical trial of landslide detection from single-temporal Landsat8 images using contour-based proposals and random forest: a case study of national Nepal[J].Landslides,2018.

Li Zhuo,He Yongjun,Li Hongen,Wang Yanqiao.Antecedent rainfall induced shallow landslide-A case study of Yunnan landslide, China[J].Scientia Iranica,2017.

Ying Liu,Linzhi Wu.Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning[J].Procedia Computer Science,2016.

Mowen Xie,Jiaxuan Huang,Liwei Wang,Jiehui Huang,Zengfu Wang.Early landslide detection based on D-InSAR technique at the Wudongde hydropower reservoir[J].Environmental Earth Sciences,2016.

A. Guarnieri,A. Masiero,A. Vettore,F. Pirotti.Evaluation of the dynamic processes of a landslide with laser scanners and Bayesian methods[J].Geomatics, Natural Hazards and Risk,2015.

Leo Breiman.Bagging Predictors[J].Machine Learning,1996(2).

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于机器学习方法改进IVM-RF耦合模型的崩滑灾害危险性评价：以延安市志丹县为例

河南省滑坡灾害易发性制图研究：多种机器学习模型的对比

融合集成机器学习与负样本采样策略的降雨群发滑坡易发性评价

基于遥感解译与信息量法的滑坡易发性评价样本优化

河南省登封南部地区滑坡灾害早期识别与易发性评价