A Novel Particulate Matter 2.5 Concentration Prediction Model Based on Double-Layer Decomposition and Feedback of Model Learning Effect

Abstract

Accurate and effective particulate matter 2.5(PM2.5) concentration prediction can provide early warning information for decision-making departments, so as to take governance and preventive measures. A combined model based on double-layer decomposition(DLD) and feedback of model learning effect for PM 2.5 concentration prediction is proposed in this paper. Firstly, ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) were used for double-level decomposition for the PM2.5 concentration series, to reduce the nonstationarity and nonlinearity of the concentration series and improve the predictability; Secondly, a Wavelet neural network (WNN) prediction model based on the feedback of model learning effect is established for the subsequence obtained by double-layer decomposition. Finally, the prediction results of each subsequence are superimposed to obtain the final prediction results. The case study shows that the prediction model proposed in this paper is scientific.