Dynamic synchronous graph transformer network for region-level air-quality forecasting

Accurate forecasting of air quality aids in mitigating air pollution, enhancing the well-being of residents, and supporting the city’s sustainable growth. Recent works have utilized graph neural network for spatial dependency modeling in air-quality forecasting task. However, many existing methods rely on separate components to individually capture temporal and spatial correlations, which makes it difficult to synchronously capture the multiscale spatiotemporal correlation (MSTCs) from the spatiotemporal graph. This paper proposed a dynamic synchronous graph transformer (DSGT) based on the Encoder-Decoder structure to forecast air quality of urban regions. It captures time-varying observed station readings through dynamic graph convolution operations and can learn the influence of auxiliary features. We designed a multiscale dynamic synchronous graph constructing way to construct graphs which can effectively encode the MSTCs. There is a multiscale spatiotemporal synchronous graph convolution component in DSGT for extracting multiscale spatiotemporal representation from the constructed graphs. The synchronous graph attention mechanism and temporal attention mechanism were designed to integrated into Encoder-Decoder structure to focus the long-term influence of auxiliary features and the short-term influence of multiscale spatiotemporal representation. Via extensive experiments on two real-world datasets, it is demonstrated that the proposed model outperforms existing methods in both short- and long-term forecasting.