Forecasting Gas-Dynamic Processes and Phenomena in Coal Mines Using Ensemble Model of Artificial Intelligence

Abstract

Predicting emergencies caused by uncontrolled and sometimes sudden changes in methane concentration within working and adjacent zones of coal mines remains a critical and challenging task, the solution for which can greatly enhance mining safety. This study presents a hybrid machine-learning model trained on real and synthetic data for accurate methane concentration forecasting and risk-level classification. The authors propose an ensemble method comprising staged data preprocessing, generation of physically meaningful features, and weighted ensembles for both regression and classification. The system is augmented with expert rules to correct forecasts and a built-in anomaly detection mechanism based on residual analysis. Experimental evaluation confirmed the model’s high performance: for regression, the coefficient of determination reached 0.984–0.997; the classifier achieved a recall of 92.8% for the rare “Accident” class under severe data imbalance (10:1). The ensemble approach reduced error variance by 40–60% compared to baseline models. The results indicate the feasibility of pilot application for dynamic early warning, which can substantially reduce coal mine accident risks.