A Novel Spatio-Temporal Graph Convolutional Network with Attention Mechanism for PM2.5 Concentration Prediction

作者：Xin Guan, Xinyue Mo, Huan Li · 发表于：Machine Learning and Knowledge Extraction · 年份：2025 · DOI：10.3390/make7030088 · 被引用次数：8 · 研究领域：Air Quality Monitoring and Forecasting、Air Quality and Health Impacts、Atmospheric chemistry and aerosols

Accurate and high-resolution spatio-temporal prediction of PM2.5 concentrations remains a significant challenge for air pollution early warning and prevention. Advanced artificial intelligence (AI) technologies, however, offer promising solutions to this problem. A spatio-temporal prediction model is designed in this study, which is built upon a seq2seq architecture. This model employs an improved graph convolutional neural network to capture spatially dependent features, integrates time-series information through a gated recurrent unit, and incorporates an attention mechanism to achieve PM2.5 concentration prediction. Benefiting from high-resolution satellite remote sensing data, the regional, multi-step and high-resolution prediction of PM2.5 concentration in Beijing has been performed. To validate the model’s performance, ablation experiments are conducted, and the model is compared with other advanced prediction models. The experimental results show our proposed Spatio-Temporal Graph Convolutional Network with Attention Mechanism (STGCA) outperforms comparison models in multi-step forecasting, achieving root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of 4.21, 3.11 and 11.41% for the first step, respectively. For subsequent steps, the model also shows significant improvements. For subsequent steps, the model also shows significant improvements, with RMSE, MAE and MAPE values of 5.08, 3.69 and 13.34% for the second step an...