山东煤炭科技2025，Vol.43Issue(4)：152-157,6.DOI:10.3969/j.issn.1005-2801.2025.04.031
几种模态分解方法在矿井涌水量预测中的研究
Research of Several Modal Decomposition Methods in Predicting Mine Water Inflow
李祥鲁 1孙文斌 1赵吉园1
作者信息
- 1. 山东科技大学能源与矿业工程学院,山东 青岛 266590
- 折叠
摘要
Abstract
To improve the accuracy of predicting mine water inflow,based on the actual measured monthly water inflow data of the mine,the influence of modal decomposition method on time series data is explored.A prediction model combining empirical modal decomposition(EMD)and long short term memory neural network(LSTM)is constructed and applied to mine water inflow prediction.Two type of new models,adaptive noise complete set empirical modal decomposition(CEEMDAN)and variational modal decomposition(VMD),are proposed for comparative analysis.The results show that the VMD-LSTM model has the highest prediction accuracy,reaching 94.14%,effectively improving the accuracy of predicting mine water inflow.The research provides a new technical path for efficient prediction of mine water inflow.关键词
矿井涌水量/时间序列预测/模态分解方法/长短期记忆神经网络Key words
mine water inflow/time series prediction/modal decomposition method/long short term memory neural network分类
矿山工程引用本文复制引用
李祥鲁,孙文斌,赵吉园..几种模态分解方法在矿井涌水量预测中的研究[J].山东煤炭科技,2025,43(4):152-157,6.
