Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks
نویسندگان
چکیده
Accurate and timely air quality weather predictions are of great importance to urban governance human livelihood. Though many efforts have been made for or prediction, most them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On hand, accurate prediction task can help improve task's performance. other geospatially distributed monitoring stations provide additional hints city-wide spatiotemporal dependency modeling. Inspired by above insights, in this paper, we propose Multi-adversarial recurrent Graph Neural Networks (MasterGNN) joint prediction. Specifically, first a heterogeneous graph neural network model autocorrelation among stations. Then, develop multi-adversarial learning framework against observation noise propagation introduced Moreover, introduce an adaptive training strategy formulating multi-task problem. Finally, extensive experiments on real-world datasets show that MasterGNN achieves best performance compared with seven baselines both
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2021
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v35i5.16529