System Development & Application
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1138-1142

Research on extrapolation of radar echo prediction sequence for rainfall prediction

Luo Jianwen1
Zou Maoyang1
Yang Hao1
Chen Min2
Yang Kangquan3
1. Chengdu University of Information Technology, Chengdu 610225, China
2. Chinese Academy of Sciences, Chengdu Institute of Computer Applications, Chengdu 610041, China
3. Sichuan Meteorological Observatory, Chengdu 610072, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0377

Abstract

The radar echo extrapolation method is widely used in rainfall forecasting. Addressing the issue of insufficient prediction accuracy in radar echoes, this paper proposed a deep learning model DIPredRNN based on recurrent neural networks. This model combined long-term temporal and channel information by introducing a dual attention mechanism of space and channel, improved the long-term dependence of time memory. By introducing an interactive framework of hidden states and inputs, it retained more features and improved the short-term dependence of temporal memory. This model was experimentally compared with classical models and many advanced models on the HKO-7 and Sichuan datasets. The model achieved the best results in comparing multiple indicators such as extrapolated images, MSE, SSIM, CSI-30~50 dbz. The experiment proves that the proposed DIPredRNN network improves the radar echo prediction performance and has advanced performance.

Foundation Support

四川省自然科学基金资助项目(2023NSFSC0482)
四川省科技计划资助项目(2022YFS0542)
成都信息工程大学科技创新能力提升计划资助项目(KYTD202324)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0377
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: System Development & Application
Pages: 1138-1142
Serial Number: 1001-3695(2024)04-026-1138-05

Publish History

[2023-11-03] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

罗健文, 邹茂扬, 杨昊, 等. 面向降雨预报的雷达回波预测序列外推方法 [J]. 计算机应用研究, 2024, 41 (4): 1138-1142. (Luo Jianwen, Zou Maoyang, Yang Hao, et al. Research on extrapolation of radar echo prediction sequence for rainfall prediction [J]. Application Research of Computers, 2024, 41 (4): 1138-1142. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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