Abstract
PM2.5 concentration is an important indicator to measure the concentration of air pollutants, and it is of important social significance and application value to realize accurate prediction of PM2.5 concentration. To further improve the accuracy of PM2.5 concentration prediction, this paper proposes a hybrid machine learning model (WD-SA-LSTM-BP model) based on simulated annealing (SA) optimization and wavelet decomposition. Firstly, the wavelet decomposition algorithm was used to realize the multiscale decomposition and single-branch reconstruction of PM2.5 concentrations to weaken the prediction error caused by time series data. Secondly, the SA optimization method was used to optimize the super-parameters of each machine learning model under each reconstructed component, so as to solve the problem that it is difficult to determine the parameters of machine learning model. Thirdly, the optimized machine learning model was used to predict the PM2.5 concentration, and the error value was calculated from the actual measured value. Then, the optimized machine learning model was used to predict the error value. Finally, the predicted error value was added to the predicted PM2.5 concentration to obtain the final predicted PM2.5 concentration. The study is experimentally validated based on daily PM2.5 data collected from Nanjing air quality monitoring stations. The experimental results showed that the RMSE and MAE values of the constructed WD-SA-LSTM-BP model were 5.26 and 3.72, respectively, which were superior to those of the WD-LSTM and LSTM models, indicating that the hybrid machine learning model based on SA optimization and wavelet decomposition could better predict the PM2.5 concentration.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Funding
This research was supported in part by the Innovative Research Group project of the National Natural Science Foundation of China (project: Cooperative Control Theory and Application of Autonomous Unmanned System (No. 61921004)).
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S.H. and P.L. conducted the research. P.L. and Q.W. revised the paper and guided the research. Y.Z. and Y.Q. were responsible for collecting data and creating the figures. Y.Y. revised and improved the paper. All authors have read and agreed to the published version of the manuscript.
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Hu, S., Liu, P., Qiao, Y. et al. PM2.5 concentration prediction based on WD-SA-LSTM-BP model: a case study of Nanjing city. Environ Sci Pollut Res 29, 70323–70339 (2022). https://doi.org/10.1007/s11356-022-20744-7
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DOI: https://doi.org/10.1007/s11356-022-20744-7