Predicting water-conducting fracture zone height in three-soft coal seams using a BOA-MLP model

The height of water-conducting fracture zone (WCFZ) critically impacts coal mine safety and water hazard control. This study develops a Bayesian Optimization Algorithm-optimized Multilayer Perceptron (BOA-MLP) model to predict WCFZ height in three-soft coal seams in Henan Province. The Analytic Hierarchy Process-Entropy Weight Method integration identified mining height, face length, and burial depth as primary factors. Trained on 32 field-measured WCFZ samples, BOA intelligently optimized hyperparameters (hidden layer neurons, learning rate, batch size). The optimal configuration (64 neurons in the first hidden layer, 32 neurons in the second hidden layer, learning rate of 0.001, batch size of 32) yielded test-set RMSE = 1.98 m, MAE = 2.23 m, MAPE = 2.67%, R2= 0.973—outperforming manual MLP with 15.0–28.5% error reductions and a 6.06% R² improvement. Statistical analysis using the Wilcoxon signed-rank test confirmed these improvements are significant (p = 0.027) with large effect size (Cohen’s d = 0.83). Comprehensive uncertainty and sensitivity analyses demonstrated the model’s robustness, with 84.4% of predictions falling within 95% confidence intervals and burial depth identified as the most influential parameter. Field validation at Panel 15,030 in Yaoling Mine confirmed the accuracy of BOA-MLP: predicted 28.1 m vs. measured 27.3 m (2.9% error), surpassing empirical formulas (18.3%) and manual MLP (7.3%). These results demonstrate that integrating Bayesian optimization substantially enhances the stability, generalizability, and predictive accuracy of the MLP model. The proposed BOA-MLP approach thus offers a robust and reliable methodology for forecasting WCFZ heights in three-soft coal seams, with significant implications for mine water management and safety planning.