A Generative Adversarial Model for Weather Radar Echo Extrapolation based on Cycle Generation Structure-Reference-Cited by-同舟云学术

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A Generative Adversarial Model for Weather Radar Echo Extrapolation based on Cycle Generation Structure

Published:2023-11-14 Issue: Volume: Page:
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Container-title:2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
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Author:

Ma Jianhao¹,Liu Qi¹,Zhang Yonghong²,Liu Xiaodong³

Affiliation:

1. School of Software Nanjing University of Information Science and Technology,Nanjing,China

2. School of Automation Nanjing University of Information Science and Technology,Nanjing,China

3. School of Computing, Edinburgh Napier University,Edinburgh,UK,ER10 5DT

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10361260/10360496/10361491.pdf?arnumber=10361491

Reference17 articles.

1. A method of radar echo extrapolation based on dilated convolution and attention convolution

2. AENN: A GENERATIVE ADVERSARIAL NEURAL NETWORK FOR WEATHER RADAR ECHO EXTRAPOLATION

3. CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

4. An Energy-Based Generative Adversarial Forecaster for Radar Echo Map Extrapolation

5. Augmented cyclegan: Learning many-to-many mappings from unpaired data;Amjad,2018

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