Coal Mine Roof Water Inrush Prediction Based on Machine Learning Research

This study develops an intelligent multi-indicator collaborative approach to improve coal seam roof water inrush warnings. A multidimensional dataset is constructed using microseismic data, borehole water levels, electrical measurements, and daily water inflow. A VMD-LSTM algorithm is proposed to predict roof rupture height, while regression analysis handles remaining indicators. Results show that during water-conducting channel development, microseismic activity, electrical data, and water inflow increase synchronously, whereas borehole water levels decline significantly—trends that reverse post-development. Compared to traditional LSTM, the VMD-LSTM model reduces MAE by 15.38%, RMSE by 20.00%, MAPE by 17.39%, HH by 9.52%, GPI by 10.76%, and improves NSE by 6.90%, demonstrating high accuracy. The central tendency prediction errors for the remaining indicators range from 0.63% to 5.73%. This integration of intelligent algorithms and multi-indicator analysis enables precise prediction of water inrush precursors, offering a new technical framework for roof water hazard prevention.