Hybrid Prediction Model of Air Pollutant Concentration for PM2.5 and PM10

Abstract

To alleviate the negative effects of air pollution, this paper explores a mixed prediction model of pollutant concentration based on the machine learning method. Firstly, in order to improve the prediction performance of the sparrow search algorithm least square support vector machine (SSA-LSSVM), a reverse learning strategy-lens principle is introduced, and a better solution is obtained by optimizing the current solution and reverse solution at the same time. Secondly, according to the nonlinear and non-stationary characteristics of the time series data of PM2.5 and PM10, the variational mode decomposition (VMD) method is used to decompose the original data to obtain the appropriate K value. Finally, experimental verification and an empirical analysis are carried out. In experiment 1, we verified the good performance of the model on University of California Irvine Machine Learning Repository (UCI) datasets. In experiment 2, we predicted the pollutant data of different cities in the Beijing–Tianjin–Hebei region in different time periods, and obtained five error results and compared them with six other algorithms. The results show that the prediction method in this paper has good robustness and the expected results can be obtained under different prediction conditions.