Prediction of PM2.5 Concentration Based on Ensemble Learning

The increase of PM2.5 is a cause of haze. Effectively predicting PM2.5 concentration and analyzing its influence factors play an important role in air quality forecasting and controlling. Considering nonlinearity and uncertainty of PM2.5 concentration, a PM2.5 concentration prediction model which firstly selects features using integrated trees was presented based on ensemble trees-gradient boosting decision tree(GBDT). With standard arithmetic mean aggregation method, the article calculates the influence degree of each feature on the increment of PM2.5 concentration, and provides the impact ranking from strong to weak. The grid-search to select the optimal parameters of the GBDT algorithm was used, such as the depth of the tree. Two datasets, the pollutant concentration data and meteorological observation data of Beijing from 2015 to 2016, are used in the prediction model proposed. Compared with standard models such as decision tree, random forest and support vector machine, the ensemble trees-GBDT model is found to be lower mean absolute errors, lower root mean square errors and better generalization ability. © 2019, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.