A GWO–Fisher Hybrid Model for Rapid and Interpretable Mine Water Inrush Source Identification with Multi-Spring Domain Validation

Rapid and accurate identification of mine water inrush sources is critical for hazard control in underground coal mining. Conventional Fisher discriminant analysis is often limited by feature redundancy and multicollinearity when applied to small-sample, high-dimensional hydrochemical data. To address this, we propose GWO–Fisher, a hybrid model integrating the Grey Wolf Optimizer (GWO) with Fisher discriminant analysis. The model employs correlation-based pre-screening followed by global optimization, using a fitness function that combines Fisher accuracy with a feature-size penalty, to achieve a compact and interpretable feature set. Trained on data from the Xiegou Coal Mine (Shanxi, China), it reduced 17 hydrochemical indicators to 12 key features, achieving 92.98% training accuracy and 86.21% test accuracy—an improvement of 10.35 percentage points over conventional Fisher. When independently validated across four mines in three spring domains, the model maintained over 83% accuracy, consistently selecting TDS, K+, and HCO3− as core features. Misclassification patterns were cross-domain consistent and linked to hydrogeological conditions. The proposed GWO–Fisher model balances predictive accuracy with hydrogeological interpretability, demonstrating reliable performance across both single-mine and cross-spring-domain scenarios.