MFNet: The Spatio-Temporal Network for Meteorological Forecasting With Architecture Search

doi:10.1109/LGRS.2022.3213618

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	MFNet: The Spatio-Temporal Network for Meteorological Forecasting With Architecture Search
	Zhang, Xinbang1,2 ; Jin, Qizhao1,2 ; Xiang, Shiming1,2 ; Pan, Chunhong1
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
	2022
卷号	19 页码:5
通讯作者	Xiang, Shiming(smxiang@nlpr.ia.ac.cn)
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.
关键词	Forecasting Computer architecture Task analysis Deep learning Convolution Correlation Wind forecasting Deep learning meteorological forecasting (MF) neural architecture search (NAS)
DOI	10.1109/LGRS.2022.3213618
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62076242]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000873801300016
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50521
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
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
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
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.