Towards realistic meteorological predictive learning using conditional GAN

Liu, Hong-Bin, and Lee, Ickjai (2020) Towards realistic meteorological predictive learning using conditional GAN. IEEE Access, 8. pp. 93179-93186.

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Abstract

Meteorological imagery prediction is an important and challenging problem for weather forecasting. It can also be seen as a video frame prediction problem that estimates future frames based on observed meteorological imageries. Despite it is a widely-investigated problem, it is still far from being solved. Current state-of-the-art deep learning based approaches mainly optimise the mean square error loss resulting in blurry predictions. We address this problem by introducing a Meteorological Predictive Learning GAN model (in short MPL-GAN) that utilises the conditional GAN along with the predictive learning module in order to handle the uncertainty in future frame prediction. Experiments on a real-world dataset demonstrate the superior performance of our proposed model. Our proposed model is able to map the blurry predictions produced by traditional mean square error loss based predictive learning methods back to their original data distributions, hence it is able to improve and sharpen the prediction. In particular, our MPL-GAN achieves an average sharpness of 52.82, which is 14% better than the baseline method. Furthermore, our model correctly detects the meteorological movement patterns that traditional unconditional GANs fail to do.

Item ID: 63911
Item Type: Article (Research - C1)
ISSN: 2169-3536
Copyright Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0
Date Deposited: 06 Aug 2020 03:41
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890202 Application Tools and System Utilities @ 100%
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