Abstract: The rolling prediction of atmospheric SO2 concentration using radial basis function (RBF) neural network based on principal component analysis (PCA) algorithm is presented. Based on the meteorological data and air quality parameters from September 1, 2019 to October 31, 2020 in Daxing district of Beijing, the PCA-RBF prediction model is constructed by combining the stepwise regression method to select the high correlation parameters between SO2 and meteorological factors as input samples. Then the PCA-RBF prediction model is used to predict the SO2 concentration in Daxing area on a certain day, and the prediction results are retained as the input parameters of the prediction model for the next day. In this way, the predicted value is continuously extended forward for the following analyzation and prediction, so as to realize the rolling prediction of SO2 concentration. Comparing the predicted results of RBF network and PCA-RBF network, the error and correlation coefficient of expected value and predicted value of PCA-RBF model are 0.03 µg·m−3 and 0.9989. It is shown that the PCA-RBF network model can accurately predict the variation trend of SO2 concentration, and provide a new technical support for further solving the air pollution problem.

Key words: stepwise regression analysis, principal component analysis, principal component analysis-radial basis function neural network, SO2