Prediction of Water-Conducting Fracture Zone Height in the Mines of Binchang Mining Area Based on Data-Driven Modeling

Given the severe water hazard in the coal seam roof of the Binchang mining area, existing research methods still primarily rely on traditional approaches such as empirical formula and numerical simulation—resulting in insufficient accuracy and convenience in predicting the height of the water-conducting fracture zone (WCFZ). By comprehensively considering three influencing factors—mining thickness, mining depth, and working face length—a data-driven approach was employed to construct a multiple nonlinear regression prediction model and a Convolutional Neural Network (CNN) prediction model based on 27 sets of measured data. Both models were subsequently applied to the ZF1403 and ZF1405 working faces in the Yadian coal mine. The results indicate that when considering only single factor of mining thickness, the coefficient of determination (R2) value of the multiple nonlinear regression model was 0.64. When considering all influencing factors, R2 improved to 0.84. The mean absolute percentage error (MAPE) of multiple nonlinear regression model was 7.52%. The established CNN model achieved a R2 of 0.97, a root mean square error (RMSE) of 9.78, and a MAPE of 4.67%. Compared to the Back Propagation Neural Network model, the prediction accuracy of the CNN model was significantly improved. The relative prediction errors of the developed height of WCFZ in the ZF1403 and ZF1405 working faces at Yadian mine were 6.30% and 2.54% for the multiple nonlinear regression model, respectively, and 0.97% and 3.15% for the CNN model, respectively. Both models met practical engineering requirements. This paper can provide reliable technical support for the prediction of water-conducting fracture zone height under mining conditions similar to the Binchang mining area.