Multi-Source Data Fusion using Graph Attention Residual GCN for Urban Air Quality Prediction

Le résumé fourni par la source

Air quality forecasting has become essential for sustainable urban management and public health. However, traditional deep spatio-temporal methods often rely on Dynamic Time Warping (DTW), which introduces high computational costs and limited scalability, making real-time forecasting challenging. Therefore, lightweight framework combining Graph Attention Networks and Residual Graph Convolutional Networks (GAT-ResGCN) is proposed for efficient, fine-grained PM2.5. Initially, meteorological time-series data were gathered from India’s Air Quality Index dataset and satellite-derived PM2.5, maps from Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) reanalysis dataset. Preprocessing included missing value imputation, Z-score normalization, and temporal alignment. Subsequently, GAT dynamically models spatial dependencies using k-nearest neighbor (k-NN) sparsification and Exponential Moving Average (EMA) updates. Furthermore, Spatio-temporal modelling was achieved through a Dynamic Spatio-Temporal Graph Convolutional Network (DSTGCN). Moreover, it integrates GAT-based graph convolutions, dilated Temporal Convolutional Networks (TCN) for long-range patterns, and temporal attention to highlight critical intervals. In addition, the Residual Network processes remote-sensing images to extract spatial pollutant patterns, which are fused with time-series features using a gated mechanism. The experimental results showed improved accuracy and scalability, achieving a root mean square error of 18.21, a Mean Absolute Error of 11.35, and an R2 of 0.92.

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