CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13176 > Article
Paper
22 May 2024 Enhancing precipitation prediction accuracy with Transformer-GAN hybrid models
Hailong Shu, Huichuang Guo, Zhen Song, Yue Wang
Author Affiliations +
Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762W (2024) https://doi.org/10.1117/12.3029025
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China
Abstract
Accurate precipitation prediction is crucial for a range of sectors, including agriculture, water resource management, and disaster preparedness. Traditional meteorological models often struggle to capture the complex spatial and temporal patterns associated with precipitation events. To address this gap, this study introduces a groundbreaking approach that combines Transformer and Generative Adversarial Network (GAN) technologies. The objective is to downscale low-resolution (25km) precipitation data to a finer resolution (8km) specifically for the Beijing region in China. Our proposed model enhances the accuracy of precipitation forecasts by leveraging a hybrid architecture that combines the strengths of Transformers and Generative Adversarial Networks (GANs). The model is particularly effective in downscaling low-resolution meteorological data to high-resolution precipitation forecasts. Comparative analyses with existing models like CorrectorGAN and ResDeepD indicate a significant improvement in forecast accuracy, validating the efficacy of our novel approach.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Hailong Shu, Huichuang Guo, Zhen Song, and Yue Wang "Enhancing precipitation prediction accuracy with Transformer-GAN hybrid models", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762W (22 May 2024); https://doi.org/10.1117/12.3029025
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 7 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Transformers
Atmospheric modeling
Meteorology
Machine learning
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top