A Comparative Study of Deep Learning-Based Models for Mine Water Inflow Prediction

Abstract

To address the need for high-precision prediction of mine water inflow under complex geological conditions, this study proposes a hybrid deep learning framework for accurate water inrush forecasting. Using daily water inrush records and nine meteorological and hydrological variables collected from the Maoping mining area in northeastern Yunnan Province, China, in 2023, five deep learning models, namely, CNN, LSTM, Transformer, CNN-LSTM, and LSTM-Transformer, were systematically developed and comparatively evaluated. The results demonstrate that the LSTM-Transformer hybrid model achieved the best predictive performance, with an MAE of 0.196, an RMSE of 0.243, and an R2 of 0.882 outperforming both the individual deep learning models and other hybrid architectures. By combining the temporal memory capability of LSTM with the global attention mechanism of Transformer, the proposed model effectively captures the nonlinear, non-stationary, and multi-scale temporal dependencies embedded in mine water inflow series, thereby substantially enhancing prediction accuracy. These findings indicate that hybrid deep learning architectures integrating local temporal memory with global attention mechanisms provide a robust and effective approach for complex engineering time-series forecasting, offering valuable support for high-precision early warning and intelligent prevention of mine water inflow hazards.