MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction

doi:10.1016/j.inffus.2024.102607

	MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction
	Yu, Chengqing 1,2; Wang, Fei 1,2; Wang, Yilun 3; Shao, Zezhi 1,2; Sun, Tao 1; Yao, Di 1; Xu, Yongjun 1,2
	2025
发表期刊	INFORMATION FUSION
ISSN	1566-2535
卷号	113 页码:15
摘要	Air quality spatiotemporal prediction can provide technical support for environmental governance and sustainable city development. As a classic multi-source spatiotemporal data, effective multi-source information fusion is key to achieving accurate air quality predictions. However, due to not fully fusing two pieces of information, classical deep learning models struggle to achieve satisfactory prediction results: (1) Multi- granularity: each air monitoring station collects air quality data at different sampling intervals, which show distinct time series patterns. (2) Spatiotemporal correlation: due to human activities and atmospheric diffusion, there exist correlations between air quality data from different air monitoring stations, necessitating the consideration of other air monitoring stations' influences when modeling each air quality time series. In this study, to achieve satisfactory prediction results, we propose the Multi-Granularity Spatiotemporal Fusion Transformer, comprised of the residual de-redundant block, spatiotemporal attention block, and dynamic fusion block. Specifically, the residual de-redundant block eliminates information redundancy between data with different granularities and prevents the model from being misled by redundant information. The spatiotemporal attention block captures the spatiotemporal correlation of air quality data and facilitates prediction modeling. The dynamic fusion block evaluates the importance of data with different granularities and integrates the prediction results. Experimental results demonstrate that the proposed model surpasses 11 baselines by 5% in performance on three real-world datasets.
关键词	Air quality prediction Multi-Granularity Spatiotemporal Fusion Transformer Spatiotemporal correlation Multi-source information fusion
DOI	10.1016/j.inffus.2024.102607
收录类别	SCI
语种	英语
资助项目	NSFC[62206266] ; NSFC[62372430] ; Youth Innovation Promotion Association CAS[2023112]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:001288156200001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39670
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Fei; Xu, Yongjun
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.China North Ind Grp Corp, Inst Nav & Control Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yu, Chengqing,Wang, Fei,Wang, Yilun,et al. MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction[J]. INFORMATION FUSION,2025,113:15.
APA	Yu, Chengqing.,Wang, Fei.,Wang, Yilun.,Shao, Zezhi.,Sun, Tao.,...&Xu, Yongjun.(2025).MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction.INFORMATION FUSION,113,15.
MLA	Yu, Chengqing,et al."MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction".INFORMATION FUSION 113(2025):15.