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Article

BiLSTM-VAE Anomaly Weighted Model for Risk-Graded Mine Water Inrush Early Warning

by
Manyu Liang
1,2,
Hui Yao
1,2,
Shangxian Yin
2,*,
Enke Hou
1,
Huiqing Lian
2,
Xiangxue Xia
2,
Jinsui Wu
3 and
Bin Xu
2
1
College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
2
Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Langfang 065201, China
3
Department of Management Science and Engineering, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(19), 10394; https://doi.org/10.3390/app151910394
Submission received: 31 July 2025 / Revised: 20 September 2025 / Accepted: 24 September 2025 / Published: 25 September 2025
(This article belongs to the Special Issue Hydrogeology and Regional Groundwater Flow)
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Abstract

A new cascaded model is proposed to improve the accuracy and early warning capability of predicting mine water inrush accidents. The model sequentially applies a Bidirectional Long Short-Term Memory Network (BiLSTM) and a Variational Autoencoder (VAE) to capture the spatio-temporal dependencies between borehole water level data and water inrush events. First, the BiLSTM predicts borehole water levels, and the prediction errors are analyzed to summarize temporal patterns in water level fluctuations. Then, the VAE identifies anomalies in the predicted results. The spatial correlation between borehole water levels, induced by the cone of depression during water inrush, is quantified to assign weights to each borehole. A weighted comprehensive anomaly score is calculated for final prediction. In actual water inrush cases from Xin’an Coal Mine, the BiLSTM-VAE model triggered high-risk alerts 9 h and 30 min in advance, outperforming the conventional threshold-based method by approximately 6 h. Compared with other models, the BiLSTM-VAE demonstrates better timeliness and higher accuracy with lower false alarm rates in mine water inrush prediction. This framework extends the lead time for implementing safety measures and provides a data-driven approach to early warning systems for mine water inrush.
Keywords: mine water inrush early warning; borehole water level prediction; anomalous water level detection; hydrogeological spatial correlation; Ordovician limestone aquifer water inrush mine water inrush early warning; borehole water level prediction; anomalous water level detection; hydrogeological spatial correlation; Ordovician limestone aquifer water inrush

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MDPI and ACS Style

Liang, M.; Yao, H.; Yin, S.; Hou, E.; Lian, H.; Xia, X.; Wu, J.; Xu, B. BiLSTM-VAE Anomaly Weighted Model for Risk-Graded Mine Water Inrush Early Warning. Appl. Sci. 2025, 15, 10394. https://doi.org/10.3390/app151910394

AMA Style

Liang M, Yao H, Yin S, Hou E, Lian H, Xia X, Wu J, Xu B. BiLSTM-VAE Anomaly Weighted Model for Risk-Graded Mine Water Inrush Early Warning. Applied Sciences. 2025; 15(19):10394. https://doi.org/10.3390/app151910394

Chicago/Turabian Style

Liang, Manyu, Hui Yao, Shangxian Yin, Enke Hou, Huiqing Lian, Xiangxue Xia, Jinsui Wu, and Bin Xu. 2025. "BiLSTM-VAE Anomaly Weighted Model for Risk-Graded Mine Water Inrush Early Warning" Applied Sciences 15, no. 19: 10394. https://doi.org/10.3390/app151910394

APA Style

Liang, M., Yao, H., Yin, S., Hou, E., Lian, H., Xia, X., Wu, J., & Xu, B. (2025). BiLSTM-VAE Anomaly Weighted Model for Risk-Graded Mine Water Inrush Early Warning. Applied Sciences, 15(19), 10394. https://doi.org/10.3390/app151910394

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