A long-term prediction method for PM2.5 concentration based on spatiotemporal graph attention recurrent neural network and grey wolf optimization algorithm

With the rapid advancement of global industrialization and urbanization, the problem of air pollution, primarily attributed to PM2.5, as the main component has become increasingly serious, directly affecting people's health. Therefore, it is necessary to forecast the long-term PM2.5 levels. However, most existing prediction methods for PM2.5 concentration lack sufficient extraction of urban spatial characteristics. Therefore, this paper proposes a novel method based on spatiotemporal graph attention recurrent neural network and grey wolf optimization (GWO-GART). Firstly, a long-term prediction model of PM2.5, the Graph Attention Cycle Network (GART), is constructed by using Graph Attention network (GAT), Graph Neural Network (GNN) and Rated Recurrent Unit (GRU) networks. In the GART model, a novel multi-GAT is proposed for the first time, which can extract the spatial features of cities circularly and obtain more implicit associations between cities. Secondly, we utilize grey wolf optimization (GWO) to select the best set of hyperparameters for the model. This automated approach aids in designing the network structure of GART. We compared GWO-GART with three single models and two hybrid models using a dataset collected from the real world. The results show that, compared with the state-of-the-art (SOTA) models, GWO-GART had an average reduction of 2.13% in RMSE, 2.47% in MAE, and an average increase of 1.55% in CSI on all datasets. This demonstrates that GWO-GART provides an efficient approach to precisely forecast long-term PM2.5 levels in urban regions.