Prediction of PM2.5 based on Elman neural network with chaos theory

Abstract

PM2.5 is difficult to accurately forecast due to the influence of multiple meteorological and pollutant variables in the complex nonlinear dynamic atmosphere system. In this paper, an Elman neural network prediction method based on chaos theory is put forward for the problem. Firstly, the chaotic characteristics of the concentration of the PM2.5 are analyzed and verified from the correlation dimension, the maximum Lyapunov exponent and the Kolmogorov entropy. Then, phase space reconstruction technique of chaotic theory is adopted to reconstruct the phase space of PM2.5 time series. The reconstructed phase space and the future concentration of PM2.5 are taken as the input and output of the Elman neural network with chaos theory (Elman-chaos) respectively. The numerical and experimental analyses show that this method is proportionally superior to that without considering the chaos characteristics and other approaches. The Elman-chaos prediction model has better prediction performance and application value.