融合三维监测与多模型分析的降雨型滑坡预警系统

doi:10.13474/j.cnki.11-2246.2026.0826

测绘通报 ›› 2026, Vol. 0 ›› Issue (8): 180-186.doi: 10.13474/j.cnki.11-2246.2026.0826

• 测绘地理信息技术应用案例 • 上一篇

融合三维监测与多模型分析的降雨型滑坡预警系统

邓于莘¹, 陈国俊¹, 陈济爱², 孔秋平³, 聂闻⁴

1. 闽西职业技术学院, 福建龙岩 364000;
2. 福建金创利信息科技发展股份有限公司, 福建福州 350000;
3. 福建永强岩土股份有限公司, 福建龙岩 364000;
4. 中钢集团马鞍山矿山研究总院股份有限公司, 安徽马鞍山 243000

收稿日期:2026-04-13 发布日期:2026-09-12
通讯作者: 陈济爱。E-mail： 151456362@qq.com
作者简介:邓于莘(1984—)，女，副教授，研究方向为BIM与数字孪生技术、智慧监测。E-mail： adi1211@163.com
基金资助:
闽西职业技术学院校级科研课题(KJMY2507)

A rainfall-induced landslide early warning system integrating 3D monitoring and multi-model analysis

Deng Yuxin¹, Chen Guojun¹, Chen Jiai², Kong Qiuping³, Nie Wen⁴

1. Minxi Vocational & Technical College, Longyan 364000, China;
2. Fujian Jinchuangli Information Technology Development Co., Ltd., Fuzhou 350000, China;
3. Fujian Yonking Geotechnical Co., Ltd., Longyan 364000, China;
4. Sinosteel Ma'anshan General Institute of Mining Research Co., Ltd., Ma'anshan 243000, China

Received:2026-04-13 Published:2026-09-12

摘要/Abstract

摘要： [目的] 针对降雨型滑坡预警中多源数据融合不足的问题，本文以福建省龙岩市为例，构建了融合三维监测与多模型分析的滑坡预警系统。[方法] 基于1984—2012年635起滑坡及同期降雨数据，确定EI-D降雨阈值曲线，验证准确率为93%；采用FR-SVM建立区域易发性评价模型，AUC值为0.85；利用无人机与六相机阵列实现边坡三维重建与形变监测；FLAC3D流-固耦合模拟表明，台风暴雨5 d内安全系数由1.676降至1.230，暴雨历时是重要变量。[结果] 该预警系统实现了区域易发性评估与单体变形监测的多层次协同。[结论] 本文可为同类地区滑坡防控提供技术参考。

关键词: 滑坡预警系统, 易发性评价, 三维监测, 数值模拟

Abstract: [Purposes] To address the insufficient integration of multi-source data in rainfall-induced landslide early warning,this study developed an early warning system integrating 3D monitoring and multi-model analysis with Longyan city,Fujian province as the study area. [Methods] Based on 635 landslide events and corresponding rainfall data from 1984 to 2012,an EI-D rainfall threshold curve was established with a validation accuracy of 93%.A regional landslide susceptibility model was constructed using the FR-SVM method,yielding an AUC of 0.85.UAV photogrammetry and a six-camera array were employed for 3D slope reconstruction and deformation monitoring.FLAC3D fluid-mechanical coupling simulation revealed that the factor of safety decreased from 1.676 to 1.230 within five days of typhoon-induced rainfall,indicating that rainfall duration is a critical variable for slope instability. [Findings] The integrated early warning system achieves multi-level synergy from regional susceptibility assessment to individual slope deformation monitoring. [Conclusions] This paper provides technical reference for landslide prevention and control in similar rainfall-prone areas．

Key words: landslide warning system, susceptibility assessment, 3D monitoring, numerical simulation

中图分类号:

P237

邓于莘, 陈国俊, 陈济爱, 孔秋平, 聂闻. 融合三维监测与多模型分析的降雨型滑坡预警系统[J]. 测绘通报, 2026, 0(8): 180-186.

Deng Yuxin, Chen Guojun, Chen Jiai, Kong Qiuping, Nie Wen. A rainfall-induced landslide early warning system integrating 3D monitoring and multi-model analysis[J]. Bulletin of Surveying and Mapping, 2026, 0(8): 180-186.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://tb.chinasmp.com/CN/10.13474/j.cnki.11-2246.2026.0826

http://tb.chinasmp.com/CN/Y2026/V0/I8/180

参考文献

[1] 中华人民共和国国家统计局.中国统计年鉴2025[EB/OL].[2026-03-23].https：//www.stats.gov.cn/sj/ndsj/2025/indexch.htm．
[2] Han Yi,Semnani S J.Important considerations in machine learning-based landslide susceptibility assessment under future climate conditions[J].Acta Geotechnica,2025,20(1):475-500.
[3] Xi Ke,Tao Pengjie,Niu Zhuangqun,et al.Three-dimensional deformation estimation from multi-temporal real-scene models for landslide monitoring[J].Remote Sensing,2024,16(15):2705.
[4] 冯娟,杨校辉,朱世奎,等.白龙江流域舟曲段立节北山大型高位滑坡运动过程分析[J].安全与环境学报,2026,26(3):992-1003．
[5] Dai Xiaomin,Liu Ziang,Liu Qihang,et al.Rainfall-adaptive landslide monitoring framework integrating FLAC3D numerical simulation and multi-sensor optimization:a case study in the Tianshan Mountains[J].Sensors,2025,25(17):5433.
[6] Chang Fengnian,Dong Shaochun,Yin Hongwei,et al.3D displacement time series prediction of a north-facing reservoir landslide powered by InSAR and machine learning[J].Journal of Rock Mechanics and Geotechnical Engineering,2025,17(7):4445-4461.
[7] 孔嘉旭,冯卫,庄建琦,等.联合机器学习模型与物理力学模型的降雨条件下兰州地区浅层黄土滑坡危险性分析[J/OL].地球科学,1-19[2026-06-15].https：//link.cnki.net/urlid/42.1874.P.20260317.1538.016．
[8] 张秋华.整体性治理视域下中等城市内涝治理研究:以福建省龙岩市为例[D].福州:福建师范大学,2024.
[9] Wilde M,Günther A,Reichenbach P,et al.Pan-European landslide susceptibility mapping:ELSUS Version 2[J].Journal of Maps,2018,14(2):97-104.
[10] Zhu Youchen,Sun Deliang,Wen Haijia,et al.Considering the effect of non-landslide sample selection on landslide susceptibility assessment[J].Geomatics,Natural Hazards and Risk,2024,15(1):2392778.
[11] Wen Haijia,Liu Lei,Zhang Jialan,et al.A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines[J].Journal of Environmental Management,2023,342:118177.
[12] Ali A.A comparative spatial statistical approach for landslide susceptibility analysis in Getara and surrounding areas,South Wollo,Northern Ethiopia[J].Discover Applied Sciences,2026,8(4):406.
[13] 曹洪洋,任晓莹,李志强.基于GIS的分形理论和信息量法的区域滑坡易发性评价[J].中国安全科学学报,2025,35(1):178-185．
[14] 张若琳,孟晖,连建发,等.基于GIS的概率比率模型的滑坡易发性评价[J].地学前缘,2010,17(6):291-297．
[15] 巨宏伟.有限元强度折减法在边坡稳定性分析中的应用[J].市政技术,2023,41(3):122-128．

融合三维监测与多模型分析的降雨型滑坡预警系统

A rainfall-induced landslide early warning system integrating 3D monitoring and multi-model analysis

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 14

编辑推荐

Metrics

本文评价

[1]	周昱辰, 喜文飞, 曹逸凡, 郭峻杞, 庄永在, 王瑞亭, 洪文玉. 联合SBAS-InSAR与ConvLSTM神经网络方法的红河断裂带北段震后滑坡易发性评价[J]. 测绘通报, 2026, 0(4): 104-111,133.
[2]	李冬, 李保罗, 杨先志, 刘宇飞, 刘玉智, 张迁. 基于非刚性配准的大跨空间结构施工位形偏差检测研究[J]. 测绘通报, 2026, 0(3): 80-85.
[3]	付林, 张俊杰, 谢明轩, 熊俊楠, 肖坤洪, 王科. 滑坡灾害时空演化过程增强可视化方法[J]. 测绘通报, 2026, 0(2): 111-117.
[4]	李文东, 叶禹, 李霞, 魏伟, 辛存林. 融合SBAS-InSAR形变与机器学习模型的滑坡易发性评价[J]. 测绘通报, 2025, 0(7): 126-131,146.
[5]	尚雪巍, 杨瑞, 陈裕汉, 高伏芳. 融合时序InSAR与PSO-SVM模型的滑坡易发性评价[J]. 测绘通报, 2025, 0(11): 70-77,123.
[6]	曹亮, 李云鹤, 陈帅, 刘顺涛, 刘伟. 基坑开挖变形特征和影响规律研究[J]. 测绘通报, 2024, 0(8): 160-164.
[7]	窦一, 侯婉清, 徐浩, 姜伟玲, 孙畅, 问瀚之, 王晓静. 北京丰台科丰桥新建雨水管涵施工期周边用地风险评估研究[J]. 测绘通报, 2024, 0(4): 96-100,106.
[8]	胡祥祥, 明璐璐, 吴涛, 刘宝康, 庞栋栋, 尹继鑫, 宋宝, 柯福阳. 融合InSAR与信息量-层次分析耦合模型的西宁市地质灾害易发性评价[J]. 测绘通报, 2023, 0(12): 51-56,75.
[9]	李坤, 赵俊三, 林伊琳, 周豹. 基于不同斜坡单元划分方法和BP神经网络的泥石流易发性评价[J]. 测绘通报, 2022, 0(8): 68-74.
[10]	王璇, 师芸, 陈浩. CF耦合LR模型的地质灾害易发性评价[J]. 测绘通报, 2022, 0(11): 112-117.
[11]	李培现, 曹禹锡, 杨中辉, 郝登程. 基于地层信息插值的FLAC3D简易建模方法[J]. 测绘通报, 2021, 0(7): 6-11.
[12]	祁于娜, 王磊. 层次分析-熵值定权法应用于山区城镇地质灾害易发性评价[J]. 测绘通报, 2021, 0(6): 112-116.
[13]	管明雷, 黄辰虎, 李清泉, 汪驰升, 丁凯. 利用水位修正模型精化潮位数值模拟[J]. 测绘通报, 2017, 0(6): 68-71.
[14]	王中山, 袁金锦. 面向灾害风险评估的台风数值模拟及可视化[J]. 测绘通报, 2015, 0(4): 108-110,132.