河南理工大学学报（自然科学版）2026，Vol.45Issue(1)：77-85,9.DOI:10.16186/j.cnki.1673-9787.2025080014
基于LSTM-Transformer模型的突水条件下矿井涌水量预测
Prediction of mine water inflow under water-inrush conditions based on a LSTM-Transformer model
摘要
Abstract
Objectives Accurate prediction of mine water inflow is crucial for preventing water hazard acci-dents 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 L1-4 limestone aqui-fer 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 cap-tures 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 wa-ter 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 predict-ing water inflow under similar geological conditions.关键词
涌水量预测/水位动态响应/LSTM-Transformer耦合模型/时间序列预测/注意力机制/矿井安全生产Key words
mine inflow prediction/dynamic response of water level/LSTM-Transformer coupled model/time series forecasting/attention mechanism/mine safety production分类
矿业与冶金引用本文复制引用
李振华,姜雨菲,杜锋,王文强..基于LSTM-Transformer模型的突水条件下矿井涌水量预测[J].河南理工大学学报（自然科学版）,2026,45(1):77-85,9.基金项目
国家自然科学基金资助项目(U24B2041,52174073) （U24B2041,52174073）
河南省高校科技创新团队支持计划项目(23IRTSTHN005) （23IRTSTHN005）
