水力发电学报2024，Vol.43Issue(5)：80-93,14.DOI:10.11660/slfdxb.20240508
施工-地质双驱动的地下洞室有害气体浓度智能预测方法
Intelligent prediction method of harmful gas concentrations in underground caverns driven by construction-geological joint impacts
摘要
Abstract
The concentration of harmful gases in underground caverns is closely related to excavation schemes and geological conditions;its accurate prediction under construction-geological impacts is crucial to construction safety management.However,it is challenging to extract useful information from harmful gas monitoring data due to the nonlinear coupling between features such as blasting parameters,bedrock types,and gas concentrations.This study presents an intelligent prediction method for harmful gas concentrations using integrated learning,incorporating the theory of SHapley Additive exPlanations(SHAP).The method conducts feature preprocessing using Principal Component Analysis(PCA),and uses the Tree-structured Parzen Estimator(TPE)algorithm to iteratively seek the optimal hyperparameters for the CatBoost(Categorical Boosting)model that is used to predict harmful gas concentrations.The SHAP explanatory framework is introduced to identify key factors affecting gas emission concentrations.Application in a case study of the diversion tunnel at the Xulong hydropower station shows that compared with the models of CatBoost,TPE-XGBoost,and TPE-LightGBM,the TPE-CatBoost model reduces the root mean square error by 48.9%,40.2%,and 36.8%respectively,demonstrating a higher prediction accuracy.Integrating the SHAP theory reveals that PM10 and PM2.5 concentrations are more closely associated with blasting schemes,while CO and CO2 concentrations are more influenced by geological conditions such as groundwater state.关键词
地下洞室/有害气体/CatBoost集成学习/TPE算法/SHAP理论Key words
underground cavity/harmful gas/CatBoost integrated learning/TPE algorithm/SHAP theory分类
水利科学引用本文复制引用
卢耕阳,陈云,聂本武,陈述,晋良海..施工-地质双驱动的地下洞室有害气体浓度智能预测方法[J].水力发电学报,2024,43(5):80-93,14.基金项目
国家自然科学基金(52209163 （）
52179136) （）
