Explainable artificial intelligence and advanced feature selection methods for predicting gas concentration in longwall mining

Accurate prediction of gas concentrations at longwall mining faces is critical for safety production, yet current methods still face challenges in interpretability and reliability. This study aims to enhance prediction accuracy and model interpretability by employing advanced feature selection techniques. We integrate Shapley Additive Explanations (SHAP) into feature selection process to identify and quantify the contributions of multivariate features to gas concentration variations. The effectiveness of SHAP-based feature selection is systematically evaluated alongside Principal Component Analysis, Dynamic Time Warping, and unfiltered features, across four baseline predictive models chosen based on their structural characteristics: Long Short-Term Memory, Gated Recurrent Unit, Transformer and Graph Neural Network. Using public dataset from the Upper Silesian coal basin in Poland, we demonstrate that models trained with SHAP-selected features outperform baseline models, particularly in terms of accuracy and reliability for long-term predictions. By identifying the most relevant features and clarifying their interactions, this study enhances predictive performance and provides deeper insights into the dynamics governing gas concentrations, emphasising the value of advanced, interpretable feature selection techniques in developing robust models for industrial applications in mining.