辽宁工程技术大学学报（自然科学版）2026，Vol.45Issue(1)：17-24,8.DOI:10.11956/j.issn.1008-0562.20250378
基于XGBoost-LSTM的光纤监测巷道变形预测
XGBoost-LSTM based prediction of tunnel deformation from optical fiber monitoring
摘要
Abstract
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.关键词
分布式光纤/煤柱内部变形/集成预测/超参数优化/误差分析Key words
distributed optical fiber/internal deformation of coal pillars/ensemble prediction/hyperparameter optimization/error analysis分类
矿业与冶金引用本文复制引用
杨健锋,雒可,柴敬,张丁丁,景超,刘永亮..基于XGBoost-LSTM的光纤监测巷道变形预测[J].辽宁工程技术大学学报（自然科学版）,2026,45(1):17-24,8.基金项目
国家自然科学基金青年科学基金项目(52004203) （52004203）
廊坊市科学技术研究与发展计划项目(2024013023) （2024013023）
中央高校基本科研业务费资助项目(3142024012) （3142024012）
