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Accurate and timely air quality and weather predictions are of greatimportance to urban governance and human livelihood. Though many efforts havebeen made for air quality or weather prediction, most of them simply employ oneanother as feature input, which ignores the inner-connection between twopredictive tasks. On the one hand, the accurate prediction of one task can helpimprove another task's performance. On the other hand, geospatially distributedair quality and weather monitoring stations provide additional hints forcity-wide spatiotemporal dependency modeling. Inspired by the above twoinsights, in this paper, we propose the Multi-adversarial spatiotemporalrecurrent Graph Neural Networks (MasterGNN) for joint air quality and weatherpredictions. Specifically, we first propose a heterogeneous recurrent graphneural network to model the spatiotemporal autocorrelation among air qualityand weather monitoring stations. Then, we develop a multi-adversarial graphlearning framework to against observation noise propagation introduced byspatiotemporal modeling. Moreover, we present an adaptive training strategy byformulating multi-adversarial learning as a multi-task learning problem.Finally, extensive experiments on two real-world datasets show that MasterGNNachieves the best performance compared with seven baselines on both air qualityand weather prediction tasks.

Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks