Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in China

• Published in: IEEE access Vol. 12; pp. 132038 - 132055
• Main Authors: Cao, Wenyi, Zhang, Rufei, Cao, Wenxin
• Format: Journal Article
• Language: English
• Published: IEEE 2024
• Subjects: Air pollution; Air quality; Air quality forecast; Atmospheric modeling; atmospheric pollutants; Data models; Deep learning; Forecasting; PatchTST-enhanced; Pollution measurement; Prediction algorithms; Predictive models; spatiotemporal distribution; Spatiotemporal phenomena

Efficient and accurate air quality forecasting contributes significantly to environmental governance, health promotion, and the development of smart cities. However, few existing models can achieve multi-scale feature fusion and long sequence time series modeling in the prediction process. This stud...