VMD-GAM-NNCom model for AQI prediction of three cities in Southwest China

Abstract

Air pollution control is significant to promote the smooth implementation of air pollution treatment and reduce the related loss of life and property. Reliable and accurate prediction can provide data support and theoretical support for air pollution control. Therefore, it is of great significance to establish reliable air pollution prediction and early warning system. Moreover, air quality index (AQI) is a comprehensive environmental pollutant monitoring index. Therefore, this research proposes a nonlinear combination based on variational mode decomposition (VMD), generalized additive model (GAM) and neutral networks for AQI, namely VMD-GAM-NNCom model. The highlight of the proposed model is introducing three nonlinear neutral networks to efficiently combine the selected individual models, which makes up for the shortage of linear combinations. What’s more, the proposed model introduces GAM to efficiently integrate decomposition modes linearly or nonlinearly. From the prediction results, it can be concluded that the proposed model VMD-GAM-beetle antennae search (BAS)-Elman has higher prediction accuracy compared with the other five comparative models, and the corresponding prediction results of the proposed model have higher consistency with the original data. Taking Baoshan as an example, the mean of absolute relative percentage error (MAPE) value of VMD-GAM-BAS-Elman is less than 6%, and reduces by over 70% on average compared with other models. Therefore, the innovative model provides an efficient and reliable nonlinear combined technique for AQI forecasting.