An Improved Attention-Based Integrated Deep Neural Network for PM2.5 Concentration Prediction

Abstract

The air quality prediction is a very important and challenging task, especially PM2.5 (particles with diameter less than 2.5 μm) concentration prediction. To improve the accuracy of the PM2.5 concentration prediction, an improved integrated deep neural network method based on attention mechanism is proposed in this paper. Firstly, the influence of exogenous series of other sites on the central site is considered to determine the best relevant site. Secondly, the data of all relevant sites are input into the improved dual-stage two-phase (DSTP) model, then the PM2.5 prediction result of each site is obtained. Finally, with the PM2.5 prediction result of each site, the attention-based layer predicts the PM2.5 concentration at the central site. The experimental results show that the proposed model is superior to most of the latest models.