MFNet: The Spatio-Temporal Network for Meteorological Forecasting With Architecture Search
文献类型：期刊论文
| 作者 | Zhang, Xinbang1,2 ; Jin, Qizhao1,2; Xiang, Shiming1,2; Pan, Chunhong1
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| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2022 |
| 卷号 | 19页码:5 |
| 关键词 | Forecasting Computer architecture Task analysis Deep learning Convolution Correlation Wind forecasting Deep learning meteorological forecasting (MF) neural architecture search (NAS) |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2022.3213618 |
| 通讯作者 | Xiang, Shiming(smxiang@nlpr.ia.ac.cn) |
| 英文摘要 | Exploiting deep learning for the meteorological forecasting (MF) task is challenging due to the complex spatio-temporal correlation, non-stationarity, and imbalanced data distribution. Though with elaborate design, handcraft hierarchical architectures adopted by current methods could be far from optimal in sufficiently modeling the dynamics of meteorological data. For the MF task, this letter presents the MFNet, which is a spatio-temporal network with the Neural Architecture Search (NAS) technique. Working in the data-driven paradigm, our method is capable of automatically generating suitable architecture to model the spatio-temporal correlation. Moreover, the nonstationarity of meteorological data is explicitly modeled through simulating spatio-temporal variations in response to the intrinsic driven force of the meteorological state, and the Error Sensitive Regression (ESR) loss is introduced accounting for the imbalanced data distribution. Extensive experiments exhibit the capability of our method and demonstrate that deep learning is potential for serving as an operational technique for global MF. |
| 资助项目 | National Natural Science Foundation of China[62076242] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000873801300016 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50520]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 通讯作者 | Xiang, Shiming |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Xinbang,Jin, Qizhao,Xiang, Shiming,et al. MFNet: The Spatio-Temporal Network for Meteorological Forecasting With Architecture Search[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5. |
| APA | Zhang, Xinbang,Jin, Qizhao,Xiang, Shiming,&Pan, Chunhong.(2022).MFNet: The Spatio-Temporal Network for Meteorological Forecasting With Architecture Search.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5. |
| MLA | Zhang, Xinbang,et al."MFNet: The Spatio-Temporal Network for Meteorological Forecasting With Architecture Search".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5. |
入库方式： OAI收割
来源：自动化研究所
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