Multi-site PM2.5 Mass Concentration Prediction Method Based on GCN-GRU-Attention
ZHU Zhenye
TANG Chaoli
Abstract：To improve the accuracy of particulate matter 2.5(PM2.5)mass concentration prediction and provide decision-making support for the formulation of air quality management strategies and the implementation of pollution control measures,the multi-site PM2.5 mass concentration prediction method based on a combined model of graph convolutional network-gated recurrent unit-attention mechanism(GCN-GRU-Attention)was proposed.The model dynamically constructed spatiotemporal graphs between monitoring sites to capture dynamic correlations and enhanced the interaction of spatiotemporal features through the attention mechanism.The results showed that,in long-term forecasting,the GCN-GRU-Attention model outperformed the best-performing PM2.5-graph neural network(PM2.5-GNN)model among the comparison models,with reduction of 3.2%in mean absolute error and 1.8%in root mean squared error.In short-term prediction and high pollution peak prediction,significant enhancements were also observed compared to traditional models and fixed graph structure models.These results demonstrated significant improvements over traditional models and fixed graph structure models.The ablation experiments further confirmed the effectiveness of dynamic graph modeling and the attention mechanism in enhancing model performance.This research provided new insights into PM2.5mass concentration prediction in dynamic environments and offered a reliable basis for air quality management and public health protection.
Keywords：PM2.5mass concentration predictiondynamic graphattention mechanismspatiotemporal feature fusionpollution treatmentAnhui Province
Publication Date：2025-03-19
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 67-72,85 )