Abstract
To enhance the accuracy of predicting the height of water-conducting fracture zones (WCFZ) in coal mines, this study proposes a novel stacked ensemble learning model. The model integrates XGBoost and Support Vector Regression (SVR) as base learners, with CatBoost serving as the meta-learner, forming a two-layer architecture. Key geological and mining features-such as mining height, burial depth, working face length, and lithologic proportion coefficient-are used as input variables to better capture the complex influencing factors. Validation using data from the No. 3 coal seam of Husheng Coal Mine demonstrates that the proposed model achieves a predicted WCFZ height of 50.79 m, closely aligning with the measured value and outperforming empirical formulas (61.4 m), standalone SVR (58.14 m), XGBoost (56.62 m), and FLAC3D simulation (55 m). The model also achieves an R² of 0.98 and RMSE of 2.08, indicating excellent predictive performance. This research is the first to introduce stacked ensemble learning for WCFZ height prediction, overcoming the limitations of single-model and simulation-based methods. The proposed approach offers a more accurate and intelligent tool for mine water hazard assessment and represents a significant advancement in applying machine learning to underground geological engineering.