Multi-view multi-task spatiotemporal graph convolutional network for air quality prediction

Accurate air quality prediction is a crucial but arduous task for intelligent cities. Predictable air quality can advise governments on environmental governance and residents on travel. However, complex correlations (i.e., intra-sensor correlation and inter-sensor correlation) make prediction challenging. Previous work considered the spatial, temporal, or combination of the two to model. However, we observe that there are also logical semantic and temporal, and spatial relations. Therefore, we propose a multi-view multi-task spatiotemporal graph convolutional network (M2) for air quality prediction. We encode three views, including spatial view (using GCN to model the correlation between adjacent stations in geographic space), logical view (using GCN to model the correlation between stations in logical space), and temporal view (using GRU to model the correlation among historical data). Meanwhile, M2 chooses a multi-task learning paradigm that includes a classification task (auxiliary task, coarse granularity prediction of air quality level) and a regression task (main task, fine granularity prediction of air quality value) to predict jointly. And the experimental results on two real-world air quality datasets demonstrate our model performances over the state-of-art methods.