Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet
IntroductionMarine forecasts play a crucial role in ensuring safe navigation, efficient offshore operations, coastal management, and research, particularly in regions with challenging conditions like the Arctic Ocean. These forecasts necessitate precise predictions of ocean currents, wind-driven wav...
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1456480/full |
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| author | Aleksei V. Buinyi Aleksei V. Buinyi Dias A. Irishev Edvard E. Nikulin Aleksandr A. Evdokimov Polina G. Ilyushina Natalia A. Sukhikh Natalia A. Sukhikh |
| author_facet | Aleksei V. Buinyi Aleksei V. Buinyi Dias A. Irishev Edvard E. Nikulin Aleksandr A. Evdokimov Polina G. Ilyushina Natalia A. Sukhikh Natalia A. Sukhikh |
| author_sort | Aleksei V. Buinyi |
| collection | DOAJ |
| description | IntroductionMarine forecasts play a crucial role in ensuring safe navigation, efficient offshore operations, coastal management, and research, particularly in regions with challenging conditions like the Arctic Ocean. These forecasts necessitate precise predictions of ocean currents, wind-driven waves, and various other oceanic parameters. Although physics-based numerical models are highly accurate, they come with significant computational requirements. Therefore, data-driven approaches, which are less computationally intensive, may present a more effective solution for predicting sea conditions.MethodsThis study introduces a detailed analysis and comparison of three data-driven models: the newly developed convLSTM-based MariNet, FourCastNet, and PhydNet, a physics-informed model designed for video prediction. Through the utilization of metrics such as RMSE, Bias, and Correlation, we illustrate the areas in which our model outperforms well-known prediction models.ResultsOur model demonstrates enhanced accuracy in forecasting ocean dynamics when compared to FourCastNet and PhyDNet. Additionally, our findings reveal that our model demands significantly less training data and computational resources, ultimately resulting in lower carbon emissions.DiscussionThese findings indicate the potential for further exploration of data-driven models as a supplement to physics-based models in operational marine forecasting, as they have the capability to improve prediction accuracy and efficiency, thereby facilitating more responsive and cost-effective forecasting systems. |
| format | Article |
| id | doaj-art-8b58e8d0a7854049b4b24d7d1d59b229 |
| institution | OA Journals |
| issn | 2296-7745 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-8b58e8d0a7854049b4b24d7d1d59b2292025-08-20T02:12:38ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452024-11-011110.3389/fmars.2024.14564801456480Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNetAleksei V. Buinyi0Aleksei V. Buinyi1Dias A. Irishev2Edvard E. Nikulin3Aleksandr A. Evdokimov4Polina G. Ilyushina5Natalia A. Sukhikh6Natalia A. Sukhikh7Research and Development Department, Marine Information Technologies LLC, Moscow, RussiaDepartment of Hydrometeorological Modeling, Lomonosov Moscow State University Marine Research Center (LMSU MRC), Moscow, RussiaResearch and Development Department, Marine Information Technologies LLC, Moscow, RussiaResearch and Development Department, Marine Information Technologies LLC, Moscow, RussiaDepartment of Hydrometeorological Research, Lomonosov Moscow State University Marine Research Center (LMSU MRC), Moscow, RussiaGeoinformation Technologies Department, Lomonosov Moscow State University Marine Research Center (LMSU MRC), Moscow, RussiaResearch and Development Department, Marine Information Technologies LLC, Moscow, RussiaDepartment of Hydrometeorological Modeling, Lomonosov Moscow State University Marine Research Center (LMSU MRC), Moscow, RussiaIntroductionMarine forecasts play a crucial role in ensuring safe navigation, efficient offshore operations, coastal management, and research, particularly in regions with challenging conditions like the Arctic Ocean. These forecasts necessitate precise predictions of ocean currents, wind-driven waves, and various other oceanic parameters. Although physics-based numerical models are highly accurate, they come with significant computational requirements. Therefore, data-driven approaches, which are less computationally intensive, may present a more effective solution for predicting sea conditions.MethodsThis study introduces a detailed analysis and comparison of three data-driven models: the newly developed convLSTM-based MariNet, FourCastNet, and PhydNet, a physics-informed model designed for video prediction. Through the utilization of metrics such as RMSE, Bias, and Correlation, we illustrate the areas in which our model outperforms well-known prediction models.ResultsOur model demonstrates enhanced accuracy in forecasting ocean dynamics when compared to FourCastNet and PhyDNet. Additionally, our findings reveal that our model demands significantly less training data and computational resources, ultimately resulting in lower carbon emissions.DiscussionThese findings indicate the potential for further exploration of data-driven models as a supplement to physics-based models in operational marine forecasting, as they have the capability to improve prediction accuracy and efficiency, thereby facilitating more responsive and cost-effective forecasting systems.https://www.frontiersin.org/articles/10.3389/fmars.2024.1456480/fullArcticmachine learningocean predictionLSTMshort-term forecast |
| spellingShingle | Aleksei V. Buinyi Aleksei V. Buinyi Dias A. Irishev Edvard E. Nikulin Aleksandr A. Evdokimov Polina G. Ilyushina Natalia A. Sukhikh Natalia A. Sukhikh Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet Frontiers in Marine Science Arctic machine learning ocean prediction LSTM short-term forecast |
| title | Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet |
| title_full | Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet |
| title_fullStr | Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet |
| title_full_unstemmed | Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet |
| title_short | Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet |
| title_sort | optimizing data driven arctic marine forecasting a comparative analysis of marinet fourcastnet and phydnet |
| topic | Arctic machine learning ocean prediction LSTM short-term forecast |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1456480/full |
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