A Municipal PM2.5 Forecasting Method Based on Random Forest and WRF Model.

In the recent years, air pollution is a very serious problem in China and elsewhere, and it is a factor that significantly affects the quality of human health. Fine particulate matter (PM2.5) is considered to be the culprit of haze weather. Therefore, research affects the quality of human life on PM2.5 forecasting has received increasing attention. Knowing this information in advance is very important to protect humans from health problems. This paper proposes a new prediction method, using the predicted value of the Weather Research and Forecasting (WRF) model as input data, increasing the atmospheric inversion factor as an additional input factor and constructing a municipal atmospheric pollutant response model through a random forest algorithm. we use 3-fold cross-validation (CV) to evaluate model performance. The result of the experiment shows, compared with the traditional atmospheric simulation method, this method has practical application significance. The simulation results have improved timeliness and accuracy. It provides a simple and effective method for PM2.5 prediction. [ABSTRACT FROM AUTHOR]