【摘要】 目的 矿井涌水量精准预测对预防矿井水害和保障矿井安全生产具有重要意义，为精准预测矿井涌水量，构建适用于华北型煤田受底板L_1-4灰岩含水层和奥陶系灰岩含水层水害威胁的矿井涌水量预测模型。方法以河南某典型矿井的水文监测数据为基础，提出LSTMTransformer模型。利用LSTM捕捉矿井涌水量的动态时序特征，通过Transformer的多头注意力机制分析含水层水位变化和矿井涌水量之间的复杂时序关联，构建水位动态变化驱动下的矿井涌水量精准预测框架。结果 结果表明，LSTM-Transformer模型预测精度显著优于LSTM,CNN,Transformer和CNN-LSTM模型的，其均方根误差为20.91 m³/h，平均绝对误差为16.08 m³/h，平均绝对百分比误差为1.12%，且和单因素涌水量预测模型相比，水位-涌水量双因素预测模型预测结果更加稳定。结论 LSTM-Transformer模型成功克服传统方法在捕捉复杂水文地质系统中水位-涌水量动态关联上的局限，为矿井涌水量动态预测提供可解释性强、鲁棒性好的解决方案，也为类似地质条件下矿井涌水量预测提供了新方法。更多还原

【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.更多还原