Prediction of PM2.5 Concentration Based on NDFA-LSSVM Model

At present, there are widespread air pollution problems in most parts of China, the accurate prediction of atmospheric pollutant concentration has become a hot issue for people to study. This paper proposes the NDFA-LSS VM model to predict the concentration of PM2.5. The hyper-parameter of Least Square Support Vector Machine ($LS$ SVM) were optimized by using the New Dynamic Firefly Algorithm (NDFA) to establish a PM2.5 concentration prediction model NDFA-LSSVM. The air quality data of monitoring stations at Chaoyang Agricultural Exhibition Hall District was used as source data to compare the performance of the optimized model with LSSVM model and General Regression Neural Network (GRNN) model. The experimental results show that the NDFA-LSSVM model proposed in this paper effectively improves the prediction accuracy of PM2.5 concentration.