PM_2.5 concentration prediction based on pseudo-F statistic feature selection algorithm and support vector regression

Effective monitoring and prediction of PM_2.5 concentration is of great practical significance to People's Daily life and social development. In order to facilitate accurate prediction of the average daily concentration of PM_2.5, a Feature Selection Algorithm Based on Pseudo F Statistic (FSPF) was proposed in this paper, and a PM_2.5 concentration prediction model was combined with support vector regression. When constructing the model, all features are not selected into the model, and the pseudo-F statistic featu re selection algorithm is used to obtain more important feature variables related to PM_2.5 concentration. On this basis, support vector regression based on different kernel functions is used to predict PM_2.5 concentration. Through real data analysis, the hyperbolic tangent kernel support vector regression model (FSPF-SVR) selected by pseudo-F statistics features has the highest prediction accuracy when predicting the average PM_2.5 concentration of Beijing in November and December 2017, with mean square errors of 1.441⋅10⁻³mg²/m⁶ and 1.307⋅10⁻³mg²/m⁶ respectively, which is better than the all-variable prediction model. At the same time, it is also better than random forest, weighted K nearest neighbor, Bagging regression, artificial neural network and other current mainstream machine learning regression prediction algorithms.