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A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars

Yang, Zhiyun; Wu, Hao; Liu, Qi; Liu, Xiaodong; Zhang, Yonghong; Cao, Xuefei

Authors

Zhiyun Yang

Hao Wu

Qi Liu

Yonghong Zhang

Xuefei Cao



Abstract

In recent years, the number of weather-related disasters significantly increases across the world. As a typical example, short-range extreme precipitation can cause severe flooding and other secondary disasters, which therefore requires accurate prediction of extent and intensity of precipitation in a relatively short period of time. Based on the echo extrapolation of networked weather radars (i.e., the Internet of Radars), different solutions have been presented ranging from traditional optical-flow methods to recent deep neural networks. However, these existing networks focus on local features of echo variations to model the dynamics of holistic radar echo motion, so it often suffers from inaccurate extrapolation of the radar echo motion trend, trajectory, and intensity. To address the problem, this paper introduces the self-attention mechanism and an extra memory that saves global spatiotemporal feature into the original Spatiotemporal LSTM (ST-LSTM) to form a self-attention Integrated ST-LSTM recurrent unit (SAST-LSTM), capturing both spatial and temporal global features of radar echo motion. And several these units are stacked to build the radar echo extrapolation network SAST-Net. Comparative experiments show that the proposed model has better performance on different real world radar echo datasets over other recent methods.

Journal Article Type Article
Acceptance Date Jun 29, 2022
Online Publication Date Jul 4, 2022
Publication Date 2023-01
Deposit Date Jul 13, 2022
Publicly Available Date Jul 18, 2022
Journal ISA Transactions
Print ISSN 0019-0578
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 132
Pages 155-166
DOI https://doi.org/10.1016/j.isatra.2022.06.046
Keywords Radar echo extrapolation, Self-attention, Long short-term memory, Spatiotemporal prediction
Public URL http://researchrepository.napier.ac.uk/Output/2886498

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