Coal and gas outburst risk level prediction model based on Bayesian theory and its application

Abstract Coal and gas outburst poses a significant threat to mine safety, requiring effective risk level prediction methods. To enhance the accuracy and intelligence of outburst risk prediction, a prediction indices system of outburst risk integrating both prior and current information is constructed, the outburst risk level classification method based on Bayesian–Gaussian mixture model is proposed, the outburst risk level prediction model based on Bayesian neural network is established. The proposed methodology is validated using field data from an engineering site. The results show that the outburst risk can be classified into four risk levels: level I, level II, level III and level IV. The model achieves prediction accuracies of 97.94%, 97.26%, 96.36%, and 90.91% for different time windows (1 h, 2 h, 4 h, and 8 h, respectively). The 2 h time window is identified as optimal, ensuring over 90% accuracy across all risk levels, including 100% for level IV. Furthermore, the model successfully predicts abnormal events such as drilling anomalies, abnormal effect test, and gas emission at the Ji 15 -15 080 machine roadway in No.8 Coal Mine. Beyond risk level prediction, the model quantifies the probability of each risk level, providing a more interpretable decision-making for mine safety management. The research contributes to coal and gas outburst disaster prevention and provide valuable insights for enhancing mining safety.