PSO Hammerstein Model Based PM2.5 Concentration Forecasting

With the continuous improvement of living standards and the progress of science and technology, the problems of air pollution are very serious and research on the forecasting of PM2.5 concentration is particularly important. In this paper, we describe the development of a method to forecast the daily average PM2.5 concentration of Ningbo. The research is based upon the measurements from eight urban center monitoring sites during the period of 2013-2014. By normalizing the sample data and using the PCA method to reduce the its dimension, a multi-input single-output Hammerstein model for forecasting the concentration of PM2.5 is established by using PSO algorithm to identify the parameters of the model. According to the results reported here, the PSO Hammerstein model can be used to predict the PM2.5 concentration of regional air well.