Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model

Abstract

Abstract Air pollution is an important issue affecting sustainable development in China, and accurate air quality prediction has become an important means of air pollution control. At present, traditional methods, such as deterministic and statistical approaches, have large prediction errors and cannot provide effective information to prevent the negative effects of air pollution. Therefore, few existing methods obtain accurate air pollutant time series predictions. To this end, a deep learning-based air pollutant prediction method, namely, the autocorrelation error-Informer (AE-Informer) model, is proposed in this study. The model implements the autocorrelation error (AE) based on the Informer model. The AE-Informer model is used to predict the hourly concentrations of multiple air pollutants, including PM10, PM2.5, NO2, and O3. The experimental results show that the mean absolute error (MAE) and root mean square error (RMSE) values of AE-Informer in multivariate prediction are 3% less than those of the Informer model; thus, the prediction error is effectively reduced. In addition, a stacking ensemble model is proposed to supplement the missing air pollutant time series data. This study uses Henan Province in China as an example to test the validity of the proposed methodology.