Short-term PM2.5 Forecasting with a Hybrid Model Based on Ensemble GRU Neural Network

Abstract

PM2.5 (particular matter with a diameter of 2.5µm or less) is one of the most important indicators of air pollution. In the field of environmental science, how to forecast PM2.5 is an important topic. We construct a previous 24-hour indicator before the predicted point to construct an enhanced dataset for PM2.5 concentration prediction. However, with a large scale of features, the performances of fundamental neural networks are not stable or accurate enough. As a result, an ensemble GRU (Gate Recurrent Unit) neural network is proposed for short-term PM2.5 prediction. This approach can improve accuracy while maintaining stability by combining the outputs after varying training. In this study, a dataset, which recording 6 indicators (PM2.5, PM10, CO, NO2, O3, SO2) for more than 20,000 hours in Shenzhen, is adopted to evaluate the proposed approach. Experimental results indicate that the proposed ensemble GRU model provides the lowest scores in MSE, RMSE criteria, and the best average-results in R2, MSE, RMSE scores.