A novel spatiotemporal multigraph convolutional network for air pollution prediction

With the industrialization of society, air pollution has become a critical environmental issue, leading to excessive morbidity and mortality from cardiovascular and respiratory diseases in humans. Accurate air pollution prediction has strongly promoted air quality control, which is important for human health. However, previous studies have failed to model spatiotemporal dependencies simultaneously with non-Euclidean distributions considering meteorological factors. In this study, a novel multigraph convolutional neural network for air pollution prediction is proposed. First, a spatial graph, an air pollution pattern graph and a meteorological pattern graph are constructed to model different relationships among non-Euclidean areas. Second, the graph convolutional network is applied to learn and incorporate the information of neighbour nodes of the corresponding graph, and then the graphs after convolution are fused. Finally, the fused matrix of GCNs is input into the gate recurrent units to capture temporal dependencies. Experimental results on the real dataset collected at air quality monitoring stations in Beijing validate the effectiveness of our proposed model.