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

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.

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Publication Details

Journal
Applied Sciences
Published
2025-08-28
DOI
https://doi.org/10.3390/app15179472
Citations
1
Primary Topic
Geoscience and Mining Technology
Type
article
Field-Weighted Citation Impact
1.30

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article

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

Shuning Dong, Jia Ding, Lei Zhu, Xiaoming Guo
1 citations
Applied Sciences
Geoscience and Mining Technology
1.30
article

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

Shuning Dong, Jia Ding, Lei Zhu, Xiaoming Guo
article en
1 citations

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.

Applied SciencesVol. 15(17)
China Coal Technology and Engineering Group Corp (China) (CN)
China Coal Technology Engineering Group, National Natural Science Foundation of China, Xi'an Research Institute, China Coal Technology Engineering Group, Natural Science Basic Research Program of Shaanxi Province
Openalex Percentile: Top 17%
Geoscience and Mining Technology
1.30
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