Forecasting Vertical Profiles of Ocean Currents from Surface Characteristics: A Multivariate Multi-Head Convolutional Neural Network–Long Short-Term Memory Approach Article Swipe
Soumyashree Kar
,
Jason McKenna
,
Glenn Anglada
,
Vishwamithra Sunkara
,
Robert Coniglione
,
Steve Stanic
,
Landry Bernard
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/jmse11101964
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/jmse11101964
While study of ocean dynamics usually involves modeling deep ocean variables, monitoring and accurate forecasting of nearshore environments is also critical. However, sensor observations often contain artifacts like long stretches of missing data and noise, typically after an extreme event occurrence or some accidental damage to the sensors. Such data artifacts, if not handled diligently prior to modeling, can significantly impact the reliability of any further predictive analysis. Therefore, we present a framework that integrates data reconstruction of key sea state variables and multi-step-ahead forecasting of current speed from the reconstructed time series for 19 depth levels simultaneously. Using multivariate chained regressions, the reconstruction algorithm rigorously tests from an ensemble of tree-based models (fed only with surface characteristics) to impute gaps in the vertical profiles of the sea state variables down to 20 m deep. Subsequently, a deep encoder–decoder model, comprising multi-head convolutional networks, extracts high-level features from each depth level’s multivariate (reconstructed) input and feeds them to a deep long short-term memory network for 24 h ahead forecasts of current speed profiles. In this work, we utilized Viking buoy data, and demonstrated that with limited training data, we could explain an overall 80% variation in the current speed profiles across the forecast period and the depth levels.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmse11101964
- https://www.mdpi.com/2077-1312/11/10/1964/pdf?version=1697023057
- OA Status
- gold
- Cited By
- 4
- References
- 59
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387528601Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jmse11101964Digital Object Identifier
- Title
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Forecasting Vertical Profiles of Ocean Currents from Surface Characteristics: A Multivariate Multi-Head Convolutional Neural Network–Long Short-Term Memory ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-11Full publication date if available
- Authors
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Soumyashree Kar, Jason McKenna, Glenn Anglada, Vishwamithra Sunkara, Robert Coniglione, Steve Stanic, Landry BernardList of authors in order
- Landing page
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https://doi.org/10.3390/jmse11101964Publisher landing page
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https://www.mdpi.com/2077-1312/11/10/1964/pdf?version=1697023057Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2077-1312/11/10/1964/pdf?version=1697023057Direct OA link when available
- Concepts
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Multivariate statistics, Buoy, Computer science, Convolutional neural network, Missing data, Hindcast, Deep learning, Artificial intelligence, Machine learning, Geology, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 2, 2024: 2Per-year citation counts (last 5 years)
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59Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.pdf_url | https://www.mdpi.com/2077-1312/11/10/1964/pdf?version=1697023057 |
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| primary_location.raw_source_name | Journal of Marine Science and Engineering |
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| publication_date | 2023-10-11 |
| publication_year | 2023 |
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| corresponding_institution_ids | https://openalex.org/I44854399 |
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