An Air Quality Grade Forecasting Approach Based on Ensemble Learning

Weike Liu, Hang Zhang, Qingbao Liu

Abstract: This paper proposes an air quality grade forecasting method based on ensemble learning. First, the training data sets are formed of the air quality data and related meteorological data crawled from air quality data website. After that, use the ensemble learning algorithm Leveraging Bagging to learn the training dataset and generate initial air quality grade forecasting model. And the initial forecasting model is used to make prediction on the prediction dataset. In total, the experiments test the learning algorithm both on the city scale and the station scale. Experimental results show that the proposed method has good prediction effect and good forecasting ability on the real forecast dataset.