Prediction Model for Harmful Gas Risk Levels in Non-Coal Strata Tunnels Based on SSA-CatBoost

Harmful gas is a major hazard in underground engineering construction, and accurate prediction of its risk level is essential for tunnel safety. Existing prediction methods for harmful-gas risk in non-coal strata tunnels are limited by empirical scoring, subjective indicator assignment, and insufficient quantitative characterization of reservoir performance. To address these limitations, this study proposes an SSA-CatBoost prediction model for harmful-gas risk levels in non-coal strata tunnels. Eight influencing indicators were selected as input variables. Among them, reservoir performance was quantitatively characterized by measured porosity and permeability, while the other six indicators were quantified using engineering-based scoring criteria. A database containing 138 real harmful-gas tunnel cases was constructed, and CatBoost was used as the base classifier. The Sparrow Search Algorithm was introduced to optimize the hyperparameters of CatBoost. The proposed SSA-CatBoost model achieved an average accuracy of 93.63% in five-fold cross-validation and an accuracy of 92.86% on the independent test set. Compared with CatBoost, SSA-SVM, and SSA-XGBoost, the proposed model showed the highest cross-validation accuracy. Engineering validation further showed that all selected validation samples were correctly classified. In addition, replacing empirical reservoir-performance scoring with measured porosity and permeability improved the recognition performance of adjacent risk levels, with the F1-scores of Level III and Level IV increasing from 0.667 and 0.727 to 0.909, respectively. The novelty of this study lies in integrating measured reservoir-performance parameters into a machine-learning-based harmful-gas risk prediction framework, thereby reducing the subjectivity of conventional scoring systems and improving the quantitative characterization of non-coal strata tunnel gas hazards.