Air Quality Prediction Based on Spatio-Temporal Feature Fusion over Graphs

Significant interest has been sparked in the monitoring and prediction of air quality due to the impact of air quality on human health. However, challenges arise from characterizing the complex spatial features and temporal features of monitored air quality data. In this paper, we develop an air quality forecasting model using spatio-temporal feature fusion over graphs. We use the location information of air quality monitoring stations to construct a directed graph adjacency matrix, which helps in extracting the spatial features of air quality data. A spatio-temporal feature extraction module is designed by explicitly involving the graph adjacency matrix to help characterize the coupled effects between spatial and temporal features of air quality data. Our proposed air quality prediction model was demonstrated using a real-world dataset collected over 35 air monitoring stations in Beijing. Numerical experiments demonstrate that our proposed model improves the air quality prediction over several existing models, e.g., 18.65 percent improvement in 24 h air quality prediction over the MAE metric and 15.91 percent improvement in 24 h prediction over the RMSE metric.