Long-Term Significant Wave Height Forecasting in the Western Atlantic Ocean Using Deep Learning Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.3390/jmse13101968
This study presents a significant wave height correction model using deep learning techniques to enhance long-term wave forecast capabilities. The model utilises buoy measurements to assess the forecasting accuracy of the ECMWF 15-day forecast of significant wave height in the western Atlantic Ocean under various input conditions. The performance of different deep learning methods in modelling the wave forecast error is compared. The model predictions are validated against buoy data, revealing that the forecasting accuracy of the various deep learning methods is comparable. In addition, the model’s adaptability is examined for varying locations and water depths within the study area. The results demonstrate that the proposed method significantly improves the accuracy of the 15-day wave height forecasting and exhibits good adaptability to a vast sea area.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmse13101968
- https://www.mdpi.com/2077-1312/13/10/1968/pdf?version=1760512352
- OA Status
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- References
- 38
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4415206196Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/jmse13101968Digital Object Identifier
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Long-Term Significant Wave Height Forecasting in the Western Atlantic Ocean Using Deep LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-10-15Full publication date if available
- Authors
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Lu Zhang, Fan Jiang, Limin Huang, Dina Silva, Wenyang Duan, C. Guedes SoaresList of authors in order
- Landing page
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https://doi.org/10.3390/jmse13101968Publisher landing page
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https://www.mdpi.com/2077-1312/13/10/1968/pdf?version=1760512352Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2077-1312/13/10/1968/pdf?version=1760512352Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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38Number of works referenced by this work
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| abstract_inverted_index.learning | 11, 52, 79 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.proposed | 105 |
| abstract_inverted_index.utilises | 21 |
| abstract_inverted_index.addition, | 84 |
| abstract_inverted_index.compared. | 61 |
| abstract_inverted_index.different | 50 |
| abstract_inverted_index.locations | 92 |
| abstract_inverted_index.long-term | 15 |
| abstract_inverted_index.modelling | 55 |
| abstract_inverted_index.model’s | 86 |
| abstract_inverted_index.revealing | 70 |
| abstract_inverted_index.validated | 66 |
| abstract_inverted_index.correction | 7 |
| abstract_inverted_index.techniques | 12 |
| abstract_inverted_index.comparable. | 82 |
| abstract_inverted_index.conditions. | 46 |
| abstract_inverted_index.demonstrate | 102 |
| abstract_inverted_index.forecasting | 27, 73, 116 |
| abstract_inverted_index.performance | 48 |
| abstract_inverted_index.predictions | 64 |
| abstract_inverted_index.significant | 4, 35 |
| abstract_inverted_index.adaptability | 87, 120 |
| abstract_inverted_index.measurements | 23 |
| abstract_inverted_index.capabilities. | 18 |
| abstract_inverted_index.significantly | 107 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.46791306 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |