PM2.5 Concentration Prediction Based on Markov Blanke Feature Selection and Hybrid Kernel Support Vector Regression Optimized by Particle Swarm Optimization

This study employed air quality and meteorological data as research materials and extracted the optimal feature subset by using the approximate Markov blanket-based normal maximum relevance minimum redundancy (nMRMR) algorithm to serve as the input data of the prediction model. In addition, a hybrid kernel (HK) was created to improve upon the traditional support vector regression (SVR) model. Particle swarm optimization (PSO) was used to calculate the optimal parameters of hybrid kernel (HK) SVR, which were then used to establish the nMRMR-PSO-HK-SVR model for PM2.5 concentration prediction. The 2016-2019 year air quality and weather data of Wuhan and Tianjin were employed to test the proposed method. The experimental results show that the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and Theil's inequality coefficient (TIC) of nMRMR-PSO-HK-SVR model are lower than those of SVR, PSO-SVR, nMRMR-SVR and PSO-HK-SVR model. But also, the proposed model could more precisely track moments of sudden PM2.5 concentration change. Thus, the nMRMR-PSO-HK-SVR model has more satisfactory generalizability and can predict PM2.5 concentration more precisely.