基于XGBoost-LSTM的光纤监测巷道变形预测OA

To address the problem of roadway instability caused by deformation of sectional coal pillars beneath remnant pillars during close-distance coal seam mining,a distributed optical fiber sensing method was employed by embedding fibers inside the coal pillars.Strain data from five monitoring boreholes were systematically processed and normalized.A training dataset was constructed using a sliding-window approach,and hyperparameters were optimized via grid search to develop an integrated mine pressure prediction model combining XGBoost(Extreme Gradient Boosting)and LSTM(Long Short-Term Memory)algorithms.The results show that the proposed model achieves a coefficient of determination(R²)of 0.922,with the root mean square error(RMSE)reduced to 4.215 and the mean absolute error(MAE)lowered to 2.135,demonstrating superior prediction accuracy,robustness,and generalization compared with single models such as XGBoost,LSTM,and random forest(RF).The research conclusion reveals the horizontal strain distribution law of coal pillar under the condition of secondary mining,and provides reference for the deformation prediction of section coal pillar.