What question did this study set out to answer?

To develop and evaluate machine learning models for predicting coal and gas outbursts and enhance interpretability.

January 14, 2026Sustainability0 citationsOpen Access

Machine Learning Prediction and Interpretability Analysis of Coal and Gas Outbursts

LXLong XuChina University of Mining and Technology XRXiaofeng RenTsinghua University HSHao SunChina University of Mining and Technology

Key Points

To develop and evaluate machine learning models for predicting coal and gas outbursts and enhance interpretability.
Identified geological indicators linked to outburst intensity.
Developed machine learning prediction models using various algorithms: Random Forest, Support Vector Machine, Back Propagation.
Optimized models using Bayesian hyperparameter tuning and assessed performance with ROC curve.
Implemented SHAP method for model interpretability.
The optimized XGBoost model demonstrated strong predictive performance.
Geological structure identified as the most important predictive feature for outburst risk.

Abstract

Coal and gas outbursts constitute a major hazard for mining safety, which is critical for the sustainable development of China’s energy industry. Rapid, accurate, and reliable pre-diction is pivotal for preventing and controlling outburst incidents. Nevertheless, the mechanisms driving coal and gas outbursts involve highly complex influencing factors. Four main geological indicators were identified by examining the attributes of these factors and their association to outburst intensity. This study developed a machine learning-based prediction model for outburst risk. Five algorithms were evaluated: K Nearest Neighbors (KNN), Back Propagation (BP), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Model optimization was performed via Bayesian hyperparameter (BO) tuning. Model performance was assessed by the Receiver Operating Characteristic (ROC) curve; the optimized XGBoost model demonstrated strong predictive performance. To enhance model transparency and interpretability, the SHapley Additive exPlanations (SHAP) method was implemented. The SHAP analysis identified geological structure was the most important predictive feature, providing a practical decision support tool for mine executives to prevent and control outburst incidents.

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