Interpretable-Stacking-Based Prediction of Height of Water-Conducting Fractured Zone and Its Applicability Boundary in Weakly Cemented Mining Areas in Western China

The height of a water-conducting fractured zone (WCFZ) is directly related to the design of water-preserved coal mining and water-hazard risk assessment in ecologically fragile mining areas in western China. Existing empirical formulas have limited regional adaptability, and individual machine learning models may show insufficient stability under small-sample and nonlinear data conditions. To address this issue, a heterogeneous Stacking ensemble prediction framework was constructed based on measured data from the Yushen mining area. Mining thickness, working face length, mining method, burial depth, coal seam dip angle, and hard strata proportion coefficient were selected as input variables. The base layer consisted of support vector regression (SVR), classification and regression tree (CART), random forest (RF), extreme gradient boosting (XGBoost), and back-propagation neural network (BPNN), while Ridge regression was used as the meta-learner. Under the current data split, the test set R2, RMSE, MAE, and MAPE of the Stacking model were 0.953, 10.99 m, 8.79 m, and 9.847%, respectively, indicating overall superiority over individual models and other ensemble configurations. The field validation results showed that the relative errors of the model for boreholes LD-1 and LD-2 in the fully mined area were 1.99% and 1.28%, respectively; however, an overestimation of 52.70% occurred for LD-3 in the coal-pillar-adjacent area. This indicates that the model is more suitable for the regional-scale screening of the maximum fractured-zone height and should not be directly used for fine-scale prediction in local boundary-affected zones. SHAP analysis showed that mining thickness, working face length, and hard strata proportion coefficient were the main influencing variables, and their response trends were generally consistent with key-strata control and the transition toward full-mining conditions. This study provides a reference for the rapid prediction of WCFZ height and preliminary evaluation of water-preserved coal mining in weakly cemented mining areas in western China.

Authors

Institutions

Publication Details

Journal
Processes
Published
2026-07-27
DOI
https://doi.org/10.3390/pr14152426
Primary Topic
Rock Mechanics and Modeling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Interpretable-Stacking-Based Prediction of Height of Water-Conducting Fractured Zone and Its Applicability Boundary in Weakly Cemented Mining Areas in Western China

P P Li, Zhengzheng Cao, S Y Li, Bo Hu et al.
Processes
Rock Mechanics and Modeling
article

Interpretable-Stacking-Based Prediction of Height of Water-Conducting Fractured Zone and Its Applicability Boundary in Weakly Cemented Mining Areas in Western China

P P Li, Zhengzheng Cao, S Y Li, Bo Hu, Jingxiang Shi, Xi Song, Liuwei Sun, Mingxuan Zeng
article en

Abstract

The height of a water-conducting fractured zone (WCFZ) is directly related to the design of water-preserved coal mining and water-hazard risk assessment in ecologically fragile mining areas in western China. Existing empirical formulas have limited regional adaptability, and individual machine learning models may show insufficient stability under small-sample and nonlinear data conditions. To address this issue, a heterogeneous Stacking ensemble prediction framework was constructed based on measured data from the Yushen mining area. Mining thickness, working face length, mining method, burial depth, coal seam dip angle, and hard strata proportion coefficient were selected as input variables. The base layer consisted of support vector regression (SVR), classification and regression tree (CART), random forest (RF), extreme gradient boosting (XGBoost), and back-propagation neural network (BPNN), while Ridge regression was used as the meta-learner. Under the current data split, the test set R2, RMSE, MAE, and MAPE of the Stacking model were 0.953, 10.99 m, 8.79 m, and 9.847%, respectively, indicating overall superiority over individual models and other ensemble configurations. The field validation results showed that the relative errors of the model for boreholes LD-1 and LD-2 in the fully mined area were 1.99% and 1.28%, respectively; however, an overestimation of 52.70% occurred for LD-3 in the coal-pillar-adjacent area. This indicates that the model is more suitable for the regional-scale screening of the maximum fractured-zone height and should not be directly used for fine-scale prediction in local boundary-affected zones. SHAP analysis showed that mining thickness, working face length, and hard strata proportion coefficient were the main influencing variables, and their response trends were generally consistent with key-strata control and the transition toward full-mining conditions. This study provides a reference for the rapid prediction of WCFZ height and preliminary evaluation of water-preserved coal mining in weakly cemented mining areas in western China.

ProcessesVol. 14(15)
China University of Mining and Technology (CN), Hefei Design and Research Institute of Coal Industry (China) (CN), Coal Industry Taiyuan Design and Research Institute (China) (CN), Henan Polytechnic University (CN)
Life below water
Openalex Percentile: Top 15%
Rock Mechanics and Modeling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.