Design a Hybrid Framework for Air Pollution Forecasting

Air pollution has witnessed that is an important issue regarding with people health. An excellent air pollution forecasting approach can provide warning to user for avoiding too much polluted air inhalation. Many studies have proposed the model of predicting PM2.5 concentration. How to improve the performance of the forecasting models is an important issue. As well known, ensemble learning is proved that it can improve the forecasting performance. In this paper, in order to improve the performance of air pollution forecasting, we explore an approach named hybrid model framework. which exploit the stacking scheme of ensemble learning. In this work, first we proposed the framework used to forecast the air pollution based on traditional machine approach and deep learning; and exploited Pearson correlation coefficient to calculate the correlation of various models, and finally find out the highest correlation among these models. We conducted experiments and made a comparison with four kinds of models to demonstrate the forecasting performance of hybrid model. The experimental results reveal that the hybrid model is superior than single air pollution forecasting model.