煤矿安全2024，Vol.55Issue(9)：166-172,7.DOI:10.13347/j.cnki.mkaq.20230988
基于深度学习的LSTM-GRU复合模型矿井涌水量预测方法研究
Research on mine water inflow prediction method of LSTM-GRU composite model based on deep learning
摘要
Abstract
In order to solve the problem of mine water surge prediction,we introduce deep learning theory,combine long short-term memory network(LSTM)and gated circulation unit(GRU),select mine water surge as the research object,and establish a mine wa-ter surge prediction model based on LSTM-GRU.Taking the mine water inflow of a mine in Shaanxi Province as sample data,the data set was divided into a training set and a test set using a 7∶3 ratio,and the gradient descent algorithm with good model training effect was selected to determine the network model parameters and regularization parameters.In order to prove the prediction accur-acy of the LSTM-GRU model,the prediction results were compared with those obtained by the traditional ARIMA model and the LSTM model to predict mine water gusher,respectively.The results show that:the mean absolute percentage error(RMSE),root mean square error(MAE),mean absolute error(MAPE)and coefficient of determination(R2)of the LSTM-GRU composite model are 70.51,53.4,2.80%and 0.86,indicating that the model has high prediction accuracy and reliability.The prediction effect is better than the traditional ARIMA model and LSTM model.关键词
矿井防治水/矿井涌水量预测/LSTM-GRU网络模型/ARIMA模型/LSTM模型Key words
mine water control/mine water flow prediction/LSTM-GRU network model/ARIMA model/LSTM model分类
矿业与冶金引用本文复制引用
连会青,李启兴,王瑞,夏向学,张庆,黄亚坤,任正瑞,康佳..基于深度学习的LSTM-GRU复合模型矿井涌水量预测方法研究[J].煤矿安全,2024,55(9):166-172,7.基金项目
国家自然科学基金资助项目(51774136,51974126) （51774136,51974126）
中央高校基本科研业务费资助项目(3142022003,3142014018) （3142022003,3142014018）
中央高校青年教师基金资助项目(3142021004) （3142021004）
