TFAN: Temporal-Feature correlations Attention-based Network for Urban Air Quality Prediction using Data Fusion technology

Air pollution raises a detrimental impact on human health and natural environment. Accurate prediction of air quality is crucial for effective pollution control and mitigation strategies. Numerous existing methods for analyzing the variation tendency of a specific air component primarily focus on its temporal and spatial information, neglecting the potential interactions between different attributes within the same time interval. In this paper, we propose a Temporal-Feature correlations Attention-based deep learning Network (TFAN), which incorporates data fusion technology. TFAN focuses on capturing temporal dependencies, feature correlations, and the potential relationship between temporal-feature through the Attention mechanism, and the data fusion method allows for a comprehensive consideration of multiple factors on prediction. Experimental results conducted using real-world data from Beijing City demonstrate that TFAN outperforms various baseline models in prediction accuracy for multiple pollutants by 10+%.