Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks
This paper introduces a comprehensive, data-driven framework for parametrically estimating directional ocean wave spectra from numerically simulated FPSO (Floating Production Storage and Offloading) vessel motions. Leveraging a mid-fidelity digital twin of a spread-moored FPSO vessel in the Guyana S...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/69 |
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| author | Do-Soo Kwon Sung-Jae Kim Chungkuk Jin MooHyun Kim |
| author_facet | Do-Soo Kwon Sung-Jae Kim Chungkuk Jin MooHyun Kim |
| author_sort | Do-Soo Kwon |
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| description | This paper introduces a comprehensive, data-driven framework for parametrically estimating directional ocean wave spectra from numerically simulated FPSO (Floating Production Storage and Offloading) vessel motions. Leveraging a mid-fidelity digital twin of a spread-moored FPSO vessel in the Guyana Sea, this approach integrates a wide range of statistical values calculated from the time histories of vessel responses—displacements, angular velocities, and translational accelerations. Artificial neural networks (ANNs), trained and optimized through hyperparameter tuning and feature selection, are employed to estimate wave parameters including the significant wave height, peak period, main wave direction, enhancement parameter, and directional-spreading factor. A systematic correlation analysis ensures that informative input features are retained, while extensive sensitivity tests confirm that richer input sets notably improve predictive accuracy. In addition, comparisons against other machine learning (ML) methods—such as Support Vector Machines, Random Forest, Gradient Boosting, and Ridge Regression—demonstrate the present ANN model’s superior ability to capture intricate nonlinear interdependencies between vessel motions and environmental conditions. |
| format | Article |
| id | doaj-art-61b70482ee3443e4a884e5e9ab23d2a8 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-61b70482ee3443e4a884e5e9ab23d2a82025-01-24T13:36:45ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-011316910.3390/jmse13010069Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural NetworksDo-Soo Kwon0Sung-Jae Kim1Chungkuk Jin2MooHyun Kim3Department of Ocean Engineering, Texas A&M University, Haynes Engineering Building, 727 Ross Street, College Station, TX 77843, USAFisheries Engineering Division, National Institute of Fisheries Science, 216 Gijanghaean-ro, Gijang-eup, Busan 46083, Republic of KoreaDepartment of Ocean Engineering and Marine Sciences, Florida Institute of Technology, Melbourne, FL 32901, USADepartment of Ocean Engineering, Texas A&M University, Haynes Engineering Building, 727 Ross Street, College Station, TX 77843, USAThis paper introduces a comprehensive, data-driven framework for parametrically estimating directional ocean wave spectra from numerically simulated FPSO (Floating Production Storage and Offloading) vessel motions. Leveraging a mid-fidelity digital twin of a spread-moored FPSO vessel in the Guyana Sea, this approach integrates a wide range of statistical values calculated from the time histories of vessel responses—displacements, angular velocities, and translational accelerations. Artificial neural networks (ANNs), trained and optimized through hyperparameter tuning and feature selection, are employed to estimate wave parameters including the significant wave height, peak period, main wave direction, enhancement parameter, and directional-spreading factor. A systematic correlation analysis ensures that informative input features are retained, while extensive sensitivity tests confirm that richer input sets notably improve predictive accuracy. In addition, comparisons against other machine learning (ML) methods—such as Support Vector Machines, Random Forest, Gradient Boosting, and Ridge Regression—demonstrate the present ANN model’s superior ability to capture intricate nonlinear interdependencies between vessel motions and environmental conditions.https://www.mdpi.com/2077-1312/13/1/69directional wave spectruminverse wave estimationartificial neural networkmachine learningsynthetic datadigital twin |
| spellingShingle | Do-Soo Kwon Sung-Jae Kim Chungkuk Jin MooHyun Kim Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks Journal of Marine Science and Engineering directional wave spectrum inverse wave estimation artificial neural network machine learning synthetic data digital twin |
| title | Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks |
| title_full | Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks |
| title_fullStr | Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks |
| title_full_unstemmed | Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks |
| title_short | Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks |
| title_sort | parametric estimation of directional wave spectra from moored fpso motion data using optimized artificial neural networks |
| topic | directional wave spectrum inverse wave estimation artificial neural network machine learning synthetic data digital twin |
| url | https://www.mdpi.com/2077-1312/13/1/69 |
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