An Identification Method for Coal and Gas Outburst Based on Stacking Ensemble Learning

Coal and gas outburst is a major mine disaster affected by complex coupled factors, bringing obstacles to disaster prevention. To address low accuracy and poor generalization of traditional single-algorithm prediction models, this paper constructs a two-layer Stacking ensemble learning identification model for outburst risk. RF, SVM and AdaBoost serve as base models, and WOA-LightGBM acts as the meta-model. Based on measured data of a Shanxi coal mine, Spearman correlation analysis and RF dimensionality reduction remove redundant features; Borderline-SMOTE balances imbalanced samples with few severe-risk data. Accuracy, macro-precision, recall and F1-score evaluate model performance after parameter optimization. Results show that the proposed Stacking model reaches 0.9770 accuracy, outperforming single machine learning models and other intelligent algorithms. It presents minor index fluctuations with strong stability and correctly identifies all eight practical engineering cases. Combining feature engineering and Stacking learning effectively captures nonlinear relations between influencing factors and risk levels. The model owns high precision and robustness, offering reliable technical support for coal and gas outburst prediction and control.