Three-Dimensional Spatio-Temporal Slim Weighted Generative Adversarial Imputation Network: Spatio-Temporal Silm Weighted Generative Adversarial Imputation Net to Repair Missing Ocean Current Data Article Swipe
Ya Jie Yue
,
Juan Li
,
Y. Zhang
,
Meiqi Ji
,
Jingyao Zhang
,
Rui Ma
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jmse13050911
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jmse13050911
Three-dimensional ocean observation is the foundation for accurately predicting ocean information. Although ocean observation sensor arrays can obtain internal data, their deployment is difficult, costly, and prone to component failures and environmental noise, resulting in discontinuous data. To address the severe missing data problem in three-dimensional ocean flow fields, this paper proposes an unsupervised model: Three-dimensional Spatio-Temporal Slim Weighted Generative Adversarial Imputation Network (3D-STA-SWGAIN). This method integrates spatio-temporal attention mechanisms and Wasserstein constraints. The generator captures the three-dimensional spatial distribution and vertical profile dynamic patterns through the spatio-temporal attention module, while the discriminator introduces gradient penalty constraints to prevent gradient vanishing. The generator strives to generate data that conforms to the real ocean flow field, and the discriminator attempts to identify pseudo-ocean current data samples. Through the adversarial training of the generator and the discriminator, high-quality completed data are generated. Additionally, a spatio-temporal continuity loss function is designed to ensure the physical rationality of the data. Experiments show that on the three-dimensional flow field dataset of the South China Sea, compared with methods such as GAIN, under a 50% random missing rate, this method reduces the error by 37.2%. It effectively solves the problem that traditional interpolation methods have difficulty handling non-uniform missing and spatio-temporal correlations and maintains the spatio-temporal continuity of the current field’s three-dimensional structure.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmse13050911
- https://www.mdpi.com/2077-1312/13/5/911/pdf?version=1746330601
- OA Status
- gold
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410069140
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4410069140Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/jmse13050911Digital Object Identifier
- Title
-
Three-Dimensional Spatio-Temporal Slim Weighted Generative Adversarial Imputation Network: Spatio-Temporal Silm Weighted Generative Adversarial Imputation Net to Repair Missing Ocean Current DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-04Full publication date if available
- Authors
-
Ya Jie Yue, Juan Li, Y. Zhang, Meiqi Ji, Jingyao Zhang, Rui MaList of authors in order
- Landing page
-
https://doi.org/10.3390/jmse13050911Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-1312/13/5/911/pdf?version=1746330601Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2077-1312/13/5/911/pdf?version=1746330601Direct OA link when available
- Concepts
-
Imputation (statistics), Generative grammar, Missing data, Generative adversarial network, Adversarial system, Computer science, Generative model, Artificial intelligence, Econometrics, Data mining, Statistics, Pattern recognition (psychology), Mathematics, Machine learning, Deep learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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41Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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