防灾与环境

基于易发性和降雨阈值的岩溶塌陷风险预测

  • 杨忠平 ,
  • 高宇豪 ,
  • 何科均 ,
  • 卢丙清 ,
  • 杨发祥
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  • 1.重庆大学 土木工程学院,重庆 400045;
    2.山地城镇建设与新技术教育部重点实验室,重庆 400045;
    3.库区环境地质灾害防治国家地方联合工程研究中心(重庆),重庆 400045;
    4.重庆南江工程勘察设计集团有限公司,重庆 401121
杨忠平(1981—),男,重庆忠县人,博士,教授、博士生导师,主要从事地质灾害成因机理方面的教学与研究工作。E-mail:yang-zhp@163.com

收稿日期: 2024-04-16

  网络出版日期: 2025-05-06

基金资助

广西重点研发计划项目(桂科AB24010144);国家重点研发计划项目(2021YFB3901402,2018YFC1504802);重庆市地质矿产勘查开发集团有限公司科技项目(NJK-2022-CHY-A-001)

Risk Prediction of Karst Collapse Based on Susceptibility and Rainfall Threshold

  • Yang Zhongping ,
  • Gao Yuhao ,
  • He Kejun ,
  • Lu Bingqing ,
  • Yang Faxiang
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  • 1. School of Civil Engineering, Chongqing University, Chongqing 400045, P.R. China;
    2. Key Laboratory of New Technology for Construction of Cities in Mountain Area(Chongqing University), Ministry of Education, Chongqing 400045, P.R. China;
    3. National Joint Engineering Research Center for Prevention and Control of Environmental Geological Hazards in the Reservoir Areas, Chongqing 400045, P.R. China;
    4. Chongqing Nanjiang Engineering Survey and Design Group Co., Ltd., Chongqing 401121, P.R. China

Received date: 2024-04-16

  Online published: 2025-05-06

摘要

为提高岩溶塌陷风险预测精度,以重庆市中梁山地区457处历史塌陷点为基础数据,结合模糊层次分析法与信息量法划定岩溶塌陷易发性分区,并基于考虑塌陷点密度选定的均布非塌陷点建立受试者曲线(ROC),验证了该分区的准确性。利用近期57处降雨型岩溶塌陷点历史数据建立前期有效降雨量–降雨历时(PEE-D)阈值方程,结合易发性分区构建启发式矩阵风险预测模型。通过塌陷概率同有效降雨强度和降雨历时的非线性拟合,得到降雨引发岩溶塌陷的连续概率值,进而与易发性分区耦合构建连续概率性预测模型。利用2021—2022年4次近发降雨致塌事件对以上两种岩溶塌陷预测模型进行了验证。结果表明:(1)选定不同倍数于塌陷点数的非塌陷点所得各ROC曲线下面积(AUC值)均达0.95以上,表明所划分易发性分区的准确性较高;(2)建立了中梁山地区降雨型岩溶塌陷连续概率的逻辑回归模型,该模型与临界降雨阈值模型所得离散概率结果拟合优度达0.913 5;(3)两种预测模型对4次近发塌陷事件出了基本相同的风险级别预测,但启发式矩阵风险预测模型所划分的极高风险区面积显著大于连续性概率预测模型,其预测结果偏保守。启发式矩阵风险预测模型易于实施,连续概率型风险预测模型具有更高的精度和空间辨识度。

本文引用格式

杨忠平 , 高宇豪 , 何科均 , 卢丙清 , 杨发祥 . 基于易发性和降雨阈值的岩溶塌陷风险预测[J]. 地下空间与工程学报, 2025 , 21(2) : 672 -683 . DOI: 10.20174/j.JUSE.2025.02.35

Abstract

To enhance the accuracy of karst collapse risk prediction, 457 historical collapse points in the Zhongliangshan area of Chongqing City were used as the basic data. Combined with the fuzzy analytic hierarchy process and information value method, karst collapse susceptibility zones were delineated, and the accuracy of these zones was verified using a receiver operating characteristic (ROC) curve based on evenly distributed non-collapse points selected considering the density of collapse points. A threshold equation for early effective rainfall volume and rainfall duration (PEE-D) was established using recent historical data from 57 rainfall-induced karst collapse points. This equation, along with susceptibility zones, was used to construct a heuristic matrix risk prediction model. By fitting the probability of collapse with the intensity and duration of rainfall, continuous probability values for rainfall-induced karst collapse were obtained and coupled with susceptibility zones to construct a continuous probability prediction model. These two karst collapse prediction models were validated using four recent rainfall-induced collapse events from 2021 to 2022. The results show that: (1) The area under the ROC curve (AUC value) obtained using non-collapse points selected at different multiples of the number of collapse points exceeded 0.95, indicating a high accuracy of the delineated susceptibility zones; (2) A logistic regression model for continuous probability of rainfall-induced karst collapse in the Zhongliangshan area was established, with a goodness-of-fit of 0.9135 compared to the discrete probability results obtained from the critical rainfall threshold model; (3) Both prediction models yielded similar risk level predictions for the four recent collapse events. However, the area classified as extremely high risk by the heuristic matrix risk prediction model was significantly larger than that by the continuous probability prediction model, indicating a conservative bias in its predictions. The heuristic matrix risk prediction model is easy to implement, while the continuous probability prediction model offers higher accuracy and spatial resolution.

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