Study on VMD-LSTM-based mine dust concentration prediction
LI Yongzhong
CHEN Bo
WANG Haishan
HU Shiqi
WANG Pan
ZHENG Xie
Abstract：To address the problem of insufficient accuracy in traditional prediction models caused by the nonlinear,non-stationary,and strong noise characteristics of underground coal mine dust concentration data,a hybrid mine dust concentration prediction method integrating Variational Mode Decomposition(VMD)and a Long Short-Term Memory Network(LSTM)was proposed.The raw dust concentration time series data were fed into VMD.Under the set conditions for the number of modes K and the constraint factor α,VMD decomposed the raw data into K mode components with different frequency characteristics,with each component corresponding to amplitude information in different frequency bands.The component data were then fed into LSTM and trained using a selective forgetting/input gate algorithm to output the component prediction results.The component prediction results were superposed and reconstructed to produce the final prediction result.The dust concentration data from a working face in the Sandaogou coal mine were used to analyze the effects of the constraint factor α on the VMD decomposition performance and the number of modes K on the prediction performance.The analysis results showed that:when K=5,the samples were completely decomposed by VMD,and each mode component contained detailed frequency information,allowing for a clear and intuitive analysis of the overall signal's composition;when α=2 000,the profiles of each mode component were complete and fully separated,whereas an excessively small α led to more redundant information in the independent components,and as the value of α increased,the bandwidth of the mode components continuously decreased while the resolution improved.The experimental results showed that:with K=5 and α=2 000,the error between the VMD-LSTM's predicted results and the measured values was minimal,and its MAE,MSE,RMSE,and MRE were all superior to those of other models.The VMD-LSTM model exhibits strong generalization ability and robustness for predicting nonlinear,non-stationary,and high-noise dust concentrations under complex environmental conditions.
Keywords：mine dust concentration predictionvariational mode decompositionlong short-term memory networknumber of modesconstraint factorVMD-LSTM
Publication Date：2025-09-30
Online Publishing Date：2025-10-27(First online date of this platform, not the publication date of the document)
Pages：9( 90-97,156 )