PREDICTION OF PM2.5 CONCENTRATION OF BP NEURAL NETWORK BASED ON IMPROVED PARTICLE GROUP ALGORITHM

Abstract

Abstract. There exists the shortage of low accuracy when using BP neural network model to predict PM2.5 concentration in air. An improved particle swarm optimization (IPSO) algorithm combined with BP neural network was proposed. Using the advantages of improved PSO algorithm global optimization ability, the weight and threshold of BP neural network are optimized, pollutant data and meteorological data are used as input data, PM2.5 concentration is used as output data, and IPSO-BP model is established for simulation prediction. Comparing and analyzing the IPSO-BP model, PSO-BP model and BP model, the results show that the MAE and RMSE of the IPSO-BP model are 6.94 and 8.47, respectively, and the R2 is 0.77. The accuracy test indicators are better than the PSO-BP model, and the BP model, PM2.5 concentration has the highest prediction accuracy, validating the validity of the model's prediction of PM2.5 concentration.