隧道密集山区岩溶塌陷易发性可解释机器学习模型--以中梁山为例
Interpretable Machine Learning Model for Karst Collapse Susceptibility in Tunnel-Dense Mountainous Areas: A Case Study of Zhongliang Mountain
古丽苏姆阿依·阿卜杜合力力 1向雨 1刘杰 1文海家1
作者信息
摘要
本研究旨在为隧道密集山区构建一个岩溶塌陷易发性可解释机器学习模型。以中梁山为例，基于424个正样本和2544个负样本，经地理探测器和递归特征消除（RFE）筛选出20个核心致灾因子，构建随机森林、XGBoost模型与贝叶斯优化后的随机森林与XGBoost模型。贝叶斯优化后的XGBoost的准确率=0.9877，召回率=0.9688，Kappa 系数=0.9503,均为模型中最高， AUC=0.9983,仅低于XGBoost（AUC=0.9985）。基于该模型生成五级易发性分区图，同时利用SHAP方法揭示不同易发性等级下的核心致灾因子。本研究表明岩溶塌陷是地形地貌因素与隧道工程扰动耦合结果，极高风险区沿隧道带分布，可为山区隧道沿线灾害防控提供参考。
Abstract
This study aims to develop an interpretable machine learning model for karst collapse susceptibility assessment in tunnel-dense mountainous areas. Taking Zhongliang Mountain as a case study, a dataset comprising 424 positive samples (collapse locations) and 2544 negative samples was constructed. A total of 20 core conditioning factors were selected through the dual application of the Geodetector and Recursive Feature Elimination (RFE) methods. Subsequently, original and Bayesian-optimized Random Forest (RF) and XGBoost models were developed. The evaluation results demonstrate that the Bayesian-optimized XGBoost (BO-XGBoost) model achieved the optimal comprehensive performance, yielding the highest accuracy (0.9877), recall (0.9688), and Kappa coefficient (0.9503), alongside an outstanding Area Under the Curve (AUC) of 0.9983, marginally second only to the unoptimized XGBoost (AUC = 0.9985). Based on the BO-XGBoost model, a five-tier susceptibility zonation map was generated. Furthermore, the SHapley Additive exPlanations (SHAP) approach was introduced to reveal the core conditioning factors across different susceptibility levels. The findings indicate that karst collapses in the study area are the coupled outcome of topographical conditions and tunneling disturbances. The extremely high-risk zones are prominently distributed along the tunnel corridors. This research provides a valuable methodological reference for targeted disaster prevention and mitigation along tunnel alignments in mountainous regions.关键词
岩溶塌陷/易发性评价/XGBoost/随机森林/贝叶斯优化/SHAP可解释性/中梁山Key words
Karst collapse/Susceptibility assessment/XGBoost/Random Forest/Bayesian optimization/SHAP interpretability/Zhongliang Mountain引用本文复制引用
古丽苏姆阿依·阿卜杜合力力,向雨,刘杰,文海家.隧道密集山区岩溶塌陷易发性可解释机器学习模型--以中梁山为例[EB/OL].(2026-10-16)[2026-10-18].http://www.paper.edu.cn/releasepaper/content/202610-9.学科分类
地质学
