Convolutional Residual-Attention: A Deep Learning Approach for Precipitation Nowcasting
Joint Authors
Li, Teng
Yan, Qing
Ji, Fuxin
Miao, Kaichao
Wu, Qi
Xia, Yi
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-29
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Short-term precipitation forecast in local areas based on radar reflectance images has become a hot spot issue in the meteorological field, which has an important impact on daily life.
Recently, deep learning techniques have been applied to this field, and the effect is promoted remarkably compared with traditional methods.
However, existing deep learning-based methods have not considered the problem that different areas and channels exert different influence on precipitation.
In this paper, we propose to incorporate the multihead attention into a dual-channel neural network to highlight the key areas for precipitation forecast.
Furthermore, to solve the problem of excessive loss of global information caused by the attention mechanism, the residual connection is introduced into the proposed model.
Quantitative and qualitative results demonstrate that the proposed method achieves the state-of-the-art precipitation forecast accuracy on the radar echo dataset.
American Psychological Association (APA)
Yan, Qing& Ji, Fuxin& Miao, Kaichao& Wu, Qi& Xia, Yi& Li, Teng. 2020. Convolutional Residual-Attention: A Deep Learning Approach for Precipitation Nowcasting. Advances in Meteorology،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1126950
Modern Language Association (MLA)
Yan, Qing…[et al.]. Convolutional Residual-Attention: A Deep Learning Approach for Precipitation Nowcasting. Advances in Meteorology No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1126950
American Medical Association (AMA)
Yan, Qing& Ji, Fuxin& Miao, Kaichao& Wu, Qi& Xia, Yi& Li, Teng. Convolutional Residual-Attention: A Deep Learning Approach for Precipitation Nowcasting. Advances in Meteorology. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1126950
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126950