A COMPARATIVE STUDY ON EDICTIVE EFFECT OF PM_2.5 IN BEIJING BASED ON TREE MODELS

In urban air quality forecast, the mass concentrations of PM_2.5 were influenced by the meteorological conditions and time period. This article selected Beijing as the experimental area, analysing a variety of pollutants concentration characteristics, time characteristics and weather characteristics. The data by hour of 33 air quality monitoring stations in 2019 were used to carry out the PM_2.5 forecast experiments, based on characteristics of LightGBM(light gradient boosting machine) PM_2.5 mass concentration prediction model. The results showed that compared with random forests model(RF), gradient boosting decision tree model(GBDT), XGBoost model, LightGBM model had the highest prediction accuracy of PM_2.5 concentration, XGBoost model came next, random forest model was the lowest. The accuracy of LightGBM model PM_2.5 prediction was higher than other models, R² was 0.9614, and training LightGBM model was fast and RAM needed less. LightGBM model on the five indicators were better than the rest of the model, and LightGBM model on PM_2.5 hourly prediction had better stability and application prospects.