journal article·2025-08-28Open access

Comparative Study on Prediction Methods for Water Inflow in Regional High-Intensity Water Inrush Mine Clusters: A Case Study of Xiaozhuang Coal Mine

Jia Ding, Shuning Dong, Xiaoming Guo, Bo Liu

Applied Sciences· Volume 15· Issue 17

Abstract

To address the challenges of predicting high-intensity water inflow in regional mine clusters, this study evaluates the reliability of three methods—hydrogeological analogy, dynamic water inflow prediction models, and numerical simulations—based on geological and hydrogeological conditions as well as measured water inflow data from the target mining area. The water inflow at various working faces of the target coal mine was back-calculated, and the reliability of the three methods was compared. The conclusions are as follows: (1) Under the hydrogeological conditions of high-intensity water inflow in regional mine clusters, the conventional hydrogeological analogy method exhibits high reliability in predicting water inflow at the first-mined working face, with a coefficient of determination (R2) as high as 0.95. However, its prediction error increases significantly for non-first-mined working faces, yielding R2 values of only 0.72–0.85. (2) Compared to the hydrogeological analogy method, the dynamic prediction model based on groundwater dynamics more accurately characterizes the lateral runoff recharge process of aquifers, significantly improving the prediction accuracy for non-first-mined working faces (R2 = 0.90–0.94). (3) The numerical simulation method for water inflow prediction demonstrates high reliability under various conditions, but its accuracy is highly dependent on model characterization and parameter calibration.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Geoscience and Mining Technology · Rock Mechanics and Modeling · Geotechnical and Geomechanical Engineering

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

CCTEG Xi’an Research Institute (Group) Co., Ltd., Xi’an 710077, China

State Key Laboratory of Coal Mine Disaster Prevention and Control, Xi’an 710077, China

Funding

National Natural Science Foundation of China · Natural Science Basic Research Program of Shaanxi Province · CCTEG Xi’an Research Institute (Group) Co., Ltd.

References (25)

Show source-provided references
1. Comprehensive caving water reduction mining technology for thick coal seams in extremely thick sandstone aquifers
2. Mining disaster in Brumadinho (Brazil): Social vulnerability from the perspective of the fisherman community10.1016/j.ijdrr.2024.104814 ↗
3. Dynamic mechanical mechanism and optimization approach of roadway surrounding coal water infusion for dynamic disaster prevention10.1016/j.measurement.2023.113639 ↗
4. Hua, Z., Zhang, Y., Meng, S., Wang, L., Wang, X., Lv, Y., Li, J., Ren, S., Bao, H., and Zhang, Z. (2023). Response Characteristics and Water Inflow Prediction of Complex Groundwater Systems under High-Intensity Coal Seam Mining Conditions. Water, 15.10.20944/preprints202309.0261.v1 ↗
5. Numerical Simulations of How Staged Dewatering and Mining Influence Surface Subsidence10.1007/s10230-022-00903-5 ↗
6. A Study of Interbedded Groundwater Contamination in a Mining Area and the Process of Grouting Composite Failure10.1007/s10230-023-00958-y ↗
7. Guzy, A., and Malinowska, A.A. (2020). Assessment of the Impact of the Spatial Extent of Land Subsidence and Aquifer System Drainage Induced by Underground Mining. Sustainability, 12.10.3390/su12197871 ↗
8. Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest10.1016/j.jhydrol.2022.128813 ↗
9. Risk assessment of disaster chain in multi-seam mining beneath gully topography10.1016/j.ijdrr.2024.104750 ↗
10. Ding, Y., Yin, S., Dai, Z., Lian, H., and Bu, C. (2024). Multi-Factor Prediction of Water Inflow from the Working Face Based on an Improved SSA-RG-MHA Model. Water, 16.10.3390/w16233390 ↗
11. Quantitative prediction model and prewarning system of water yield capacity (WYC) from coal seam roof based on deep learning and joint advanced detection10.1016/j.energy.2023.130200 ↗
12. Integral delay inspired deep learning model for single pool water level prediction10.1016/j.jhydrol.2025.133328 ↗
13. Predicting tunnel water inflow using a machine learning-based solution to improve tunnel construction safety10.1016/j.trgeo.2023.100978 ↗
14. An upscaling approach to predict mine water inflow from roof sandstone aquifers10.1016/j.jhydrol.2022.128314 ↗
15. Statistical analysis of the impact of ore draw strategies on wet inrush hazard susceptibility in a panel cave mine10.1016/j.ijrmms.2023.105632 ↗
16. Simulation of surface water–groundwater interaction in coal mining subsidence areas: A case study of the Kuye River Basin in China10.1016/j.jhydrol.2025.133243 ↗
17. Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques10.1016/j.autcon.2021.103719 ↗
18. Prediction of dominant roof water inrush windows and analysis of control target area based on set pair variable weight—Forward correlation cloud model10.1016/j.jclepro.2024.144253 ↗
19. Evaluating the impact of coal seam roof groundwater using variable weights theory: A special emphasis on skylight-type water inrush pattern10.1016/j.ejrh.2024.102009 ↗
20. Lian, H., Zhang, Q., Yin, S., Yan, T., Yao, H., Yang, S., Kang, J., Xia, X., Li, Q., and Huang, Y. (2024). Integrating Microseismic Monitoring for Predicting Water Inrush Hazards in Coal Mines. Water, 16.10.3390/w16081168 ↗
21. Yang, Z., Dong, D., Chen, Y., and Wang, R. (2024). Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods. Water, 16.10.3390/w16192749 ↗
22. Dynamic behavior of outburst two-phase flow in a coal mine T-shaped roadway: The formation of impact airflow and its disaster-causing effect10.1016/j.ijmst.2023.03.011 ↗
23. Research on the Dynamic Attenuation Dynamics of Double Indices of Water Gushing in the Goaf of Coal Mine Face Push Mining
24. Numerical simulation of the interaction between mine water drainage and recharge: A case study of Wutongzhuang coal mine in Heibei Province, China10.1016/j.ecolind.2024.111568 ↗
25. Enhancing mine groundwater system prediction: Full-process simulation of mining-induced spatio-temporal variations in hydraulic conductivities via modularized modeling10.1016/j.ijmst.2024.11.014 ↗