MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction
文献类型：期刊论文
| 作者 | Yu, Chengqing1,2; Wang, Fei1,2; Wang, Yilun3; Shao, Zezhi1,2; Sun, Tao1; Yao, Di1; Xu, Yongjun1,2 |
| 刊名 | INFORMATION FUSION
 |
| 出版日期 | 2025 |
| 卷号 | 113页码:15 |
| 关键词 | Air quality prediction Multi-Granularity Spatiotemporal Fusion Transformer Spatiotemporal correlation Multi-source information fusion |
| ISSN号 | 1566-2535 |
| DOI | 10.1016/j.inffus.2024.102607 |
| 英文摘要 | 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. |
| 资助项目 | NSFC[62206266] ; NSFC[62372430] ; Youth Innovation Promotion Association CAS[2023112] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001288156200001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39669]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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