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Title	Air Pollution Forecasting Using Machine Learning With Temporal Fusion Transformer And Graph Neural Networks
ID_Doc	7133
Authors	Rath S.; Maneesha P.
Year	2025
Published	3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025
DOI	http://dx.doi.org/10.1109/IDCIOT64235.2025.10914714
Abstract	Air pollution is a significant obstacle to achieving urban sustainability and public health, particularly in rapidly developing regions. Accurate air quality prediction is essential for proactive pollution management and policy formulation. In this study, we employ sophisticated machine learning frameworks, such as Temporal Fusion Transformer (TFT) and Graph Neural Networks (GNNs), to predict air quality levels. TFT is designed to capture temporal dependencies, making it ideal for time-series forecasting, while GNNs model spatial relationships, enabling the analysis of pollutant dispersion across monitoring stations .A synthetic dataset comprising 50 rows was generated, simulating pollutant concentrations (PM2.5, PM10, CO, NO2, SO2) and meteorological variables (temperature, humidity, wind speed). The models were assessed using criteria like R2, RMSE, and MAE. TFT achieved an R2 of 0.97 and an RMSE of 4.2 µg/m3, significantly outperforming traditional models like Random Forest and XGBoost. GNN provided spatial insights, identifying pollutant hotspots and their dispersion patterns. This research highlights the superior accuracy and scalability of TFT and GNN for air quality prediction. The results underline their potential for real-time monitoring, smart city integration, and proactive environmental management, offering a robust solution for addressing urban air pollution challenges. © 2025 IEEE.
Author Keywords	Air Quality Prediction; Graph Neural Networks; Machine Learning; Temporal Fusion Transformer

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