Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate has become an important research task. However, pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture air pollution relationship cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) gated recurrent unit (GRU), named GAT-GRU for prediction. Specifically, GAT used learn spatial dependence data GRU extract temporal long-term series. The proposed integrates learned spatio-temporal dependencies complex features. Considering that related meteorological conditions city, knowledge acquired from enhance performance. input consists data. In order verify effectiveness model, designs experiments on real-world datasets compared with other baselines. Experimental results prove our achieves excellent performance