矿业科学学报2025，Vol.10Issue(5)：879-889,11.DOI:10.19606/j.cnki.jmst.2025082
基于PCA-HPO-ELM的智能化矿井瓦斯涌出量预测研究
Gas emission prediction of intelligent mines based on PCA-HPO-ELM
摘要
Abstract
The intelligent development of coal mining leads to decreasing annual coal mine safety acci-dents,yet safety production still requires constant vigilance.Accurate prediction of mine gas emission is vital to ensuring safe production and improving efficiency.Conventional prediction methods are defi-cient for their complex calculation and insufficient accuracy in dealing with high-dimensional data,una-ble to satisfy modern intelligent management of coal mines.Therefore,proposes a Principal Component Analysis-Hunter Prey Optimization-Extreme Learning Machine(PCA-HPO-ELM)model for gas emis-sion prediction of intelligent mines:1)13 key influencing factors such as coal seam thickness and min-ing depth were selected,and Principal Component Analysis(PCA)was used to reduce the data from 13 dimensions to 4 dimensions.This not only reduced the dimension but also retained the main infor-mation,laying a foundation for model training;2)Hunter Prey Optimization(HPO)algorithm was in-troduced to solve the randomness of the input weights and hidden layer threshold selection of the tradi-tional Extreme Learning Machine(ELM)model,and the accurate prediction of gas emission is real-ized.PCA-HPO-ELM,PCA-PSO-ELM and PCA-ELM models were compared using the same data for the proposed models.Results show that the PCA-HPO-ELM model exhibited better iteration speed than the PCA-PSO-ELM model,and the determination coefficient R2 of predicting mine gas emission was 0.993 76,higher than that of the other two(0.988 54 and 0.894 3,respectively),showing superiori-ty;the model can be used for reference to improve the prediction accuracy and efficiency of intelligent mine gas emission.关键词
智能化矿井/瓦斯涌出量/预测模型/主成分分析/决定系数Key words
Intelligent mine/gas emission quantity/prediction model/principal component analysis/the determination coefficient分类
矿业与冶金引用本文复制引用
张科学,李伟涛,李中旭,陈学习,郑庆学,王晓玲,李小磊,侯典臣,李鑫磊,闫星辰..基于PCA-HPO-ELM的智能化矿井瓦斯涌出量预测研究[J].矿业科学学报,2025,10(5):879-889,11.基金项目
贵州省科技重大专项课题[黔科合重大专项字(2021)3001-03] （2021）
深部岩土力学与地下工程国家重点实验室(北京)开放基金(SKLGDUEK1822) （北京）
中国科协科技智库青年人才计划(20220615ZZ07110397) （20220615ZZ07110397）
中央高校基本科研业务费(3142021007,3142019009) （3142021007,3142019009）
国家自然科学基金(51804160) （51804160）
