LI Z H, JIANG YU F, DU F,et al.Prediction of mine water inflow under water-inrush conditions based on a LSTM-Transformer model[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(1):77-85.

doi:10.16186/j.cnki.1673-9787.2025080014

Received:2025/08/09

Revised:2025/10/15

Published:2025-12-03

Prediction of mine water inflow under water-inrush conditions based on a LSTM-Transformer model

Li Zhenhua1,2,3,4, Jiang Yufei1, Du Feng1,2,3,4, Wang Wenqiang1

1.School of Energy Science and Engineering， Henan Polytechnic University， Jiaozuo  454000， Henan， China；2.Collaborative Innovation Center of Coal Work Safety and Clean High Efficiency Utilization， Jiaozuo  454000， Henan，  China；3.Henan Mine Water Disaster Prevention and Control and Water Resources Utilization Engineering Technology Research Center， Henan Polytechnic University， Jiaozuo  454000， Henan， China；4.Zhengzhou Institute for Advanced Research of Henan Polytechnic University， Zhengzhou  451464， Henan， China

Abstract: Objectives Accurate prediction of mine water inflow is crucial for preventing water hazard accidents and ensuring safe production. This study aims to construct a water inflow prediction model suitable for mines in North China-type coalfields affected by water hazards from the underlying L_1-4 limestone aquifer and Ordovician limestone aquifer. Methods Based on hydrogeological monitoring data from a typical coal mine in Henan Province， a coupled LSTM-Transformer model was proposed. The LSTM component captures the dynamic temporal characteristics of mine water inflow， while the multi-head attention mechanism of the Transformer analyzes the complex temporal correlation between aquifer water level variations and mine water inflow. This framework enables accurate prediction of mine water inflow driven by dynamic water level changes. Results The coupled LSTM-Transformer model significantly outperformed LSTM， CNN， Transformer， and CNN-LSTM models in prediction accuracy， with a root mean square error （RMSE） of 20.91 m³/h， mean absolute error （MAE） of 16.08 m³/h， and mean absolute percentage error （MAPE） of 1.12%. Furthermore， compared to the single-factor water inflow prediction model， the two-factor （water level and water inflow） prediction model showed greater stability. Conclusions The LSTM-Transformer coupled model successfully overcomes the limitations of traditional methods in capturing the dynamic water level-discharge relationship within complex hydrogeological systems. It provides a solution for dynamic mine water inflow prediction with strong interpretability and robustness， offering a novel methodology for predicting water inflow under similar geological conditions.

Key words:mine inflow prediction;dynamic response of water level;LSTM-Transformer coupled model;time series forecasting;attention mechanism;mine safety production