PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks

Industrial parks are one of the main sources of air pollution; the ability to forecast PM2.5, the main pollutant in the industrial park, is of great significance to the health of the workers in the industrial park and environmental governance, which can improve the decision-making ability of environmental management. Most of the existing PM2.5 concentration forecast methods lack the ability to model the dynamic temporal and spatial correlations of PM2.5 concentration. In an industrial park environment, in order to improve the accuracy of PM2.5 concentration forecast, based on deep learning technology, this paper proposes a spatiotemporal graph convolutional network based on the attention mechanism (STAM-STGCN) to solve the PM2.5 concentration forecast problem. When constructing the adjacency matrix, we not only use the Euclidean distance between sites but also consider the impact of wind fields and the impact of pollution sources near the nodes. In the process of model construction, we first use the spatiotemporal attention mechanism to capture the dynamic spatiotemporal correlations in PM2.5 data. In the spatiotemporal convolution module, we use graph convolutional neural networks to capture spatial features and standard convolution to describe temporal features. Finally, the output module adjusts the output shape of the data to produce the final forecast result. In this paper, the mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are used as the performance evaluation metrics of the model, and the Dongmingnan Industrial Park atmospheric dataset is used to verify the effectiveness of the proposed algorithm. The experimental results show that our STAM-STGCN model can more fully capture the spatial-temporal characteristics of PM2.5 concentration data; compared with the most advanced model in the comparison model, the RMSE can be improved about 24.2%, the MAE is improved about 35.8%, and the MAPE is improved about 34.6%.