A Discriminative Model of Mine Inrush Water Source Based on Automatic Construction of Deep Belief Rule Base

Mine water inrush is a significant environmental catastrophe during the coal mining process, and the timely discrimination of the source of water inrush is the key to ensuring safe production in coal mines. This work suggests a mine water inrush—belief rule base (MWI-BRB) source discrimination model to overcome the interpretability and performance issues with conventional models. MWI-BRB firstly automatically constructs the reference values of prerequisite attributes using the Sum of Squared Errors—K-means++ algorithm, which effectively combines expert knowledge and data-driven methods, and solves the limitation of the traditional belief rule base model relying on specialist knowledge. Secondly, the hierarchical incremental structure solves the rule explosion problem caused by complex features while using XGBoost to select features. Finally, in the inference process, the model adopts an evidential reasoning algorithm to realize transparent causal inference, guaranteeing the model’s interpretability and transparency. The Penalized Covariance Matrix Adaptation Evolution Strategy algorithm optimizes the model parameters to increase the discriminative accuracy of the model even more. Experimental results on a real coal mine dataset (a total of 67 samples from Hebei, China, covering four water inrush sources) demonstrate that the proposed MWI-BRB achieves 95.23% accuracy, 95.23% recall, and 95.36% F1-score under a 7:3 training–testing split with parameter tuning performed via leave-one-out cross-validation. The near-identical values across accuracy, recall, and F1-score reflect the balanced nature of the dataset and the robustness of the model across different evaluation metrics. Compared with baseline models, MWI-BRB’s accuracy and recall are 4.78% higher than BPNN and 9.52% higher than KNN, RF, and XGBoost; its F1-score is 4.85% higher than BPNN, 10.64% higher than KNN, 10.19% higher than RF, and 9.65% higher than XGBoost. Moreover, the model maintains high interpretability. In conclusion, the MWI-BRB model can realize efficient and accurate water inrush source discrimination in complex environments, which provides a feasible technical solution for the prevention and control of mine water damage.