Applying Neural Networks to Predict Offshore Platform Dynamics
Integrating renewable energy sources with aquaculture systems on floating multi-use platforms presents an innovative approach to developing sustainable and resilient offshore infrastructure, utilizing the ocean’s considerable potential. From March 2021 to January 2022, a 1:15-scale prototype was tes...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/11/2001 |
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| author | Nikolas Martzikos Carlo Ruzzo Giovanni Malara Vincenzo Fiamma Felice Arena |
| author_facet | Nikolas Martzikos Carlo Ruzzo Giovanni Malara Vincenzo Fiamma Felice Arena |
| author_sort | Nikolas Martzikos |
| collection | DOAJ |
| description | Integrating renewable energy sources with aquaculture systems on floating multi-use platforms presents an innovative approach to developing sustainable and resilient offshore infrastructure, utilizing the ocean’s considerable potential. From March 2021 to January 2022, a 1:15-scale prototype was tested in Reggio Calabria, Italy, which gave crucial insights into how these structures behave under different wave conditions. This study investigates the application of Artificial Neural Networks (ANNs) to predict changes in mooring loads, particularly at key points of the structure. By analyzing metocean data, several ANN models and optimization techniques were evaluated to identify the most accurate predictive model. With a Normalized Root Mean Square Error (NRMSE) of 1.7–4.7%, the results show how ANNs can effectively predict offshore platform dynamics. This research highlights the potential of machine learning in developing and managing sustainable ocean systems, setting the stage for future advancements in data-driven marine resource management. |
| format | Article |
| id | doaj-art-2fd75ddff00f4ef2a08a7abbb42aac96 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-2fd75ddff00f4ef2a08a7abbb42aac962025-08-20T02:04:58ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-11-011211200110.3390/jmse12112001Applying Neural Networks to Predict Offshore Platform DynamicsNikolas Martzikos0Carlo Ruzzo1Giovanni Malara2Vincenzo Fiamma3Felice Arena4Natural Ocean Engineering Laboratory (NOEL), “Mediterranea” University of Reggio Calabria, Loc. Feo di Vito, 89122 Reggio Calabria, ItalyNatural Ocean Engineering Laboratory (NOEL), “Mediterranea” University of Reggio Calabria, Loc. Feo di Vito, 89122 Reggio Calabria, ItalyNatural Ocean Engineering Laboratory (NOEL), “Mediterranea” University of Reggio Calabria, Loc. Feo di Vito, 89122 Reggio Calabria, ItalyNatural Ocean Engineering Laboratory (NOEL), “Mediterranea” University of Reggio Calabria, Loc. Feo di Vito, 89122 Reggio Calabria, ItalyNatural Ocean Engineering Laboratory (NOEL), “Mediterranea” University of Reggio Calabria, Loc. Feo di Vito, 89122 Reggio Calabria, ItalyIntegrating renewable energy sources with aquaculture systems on floating multi-use platforms presents an innovative approach to developing sustainable and resilient offshore infrastructure, utilizing the ocean’s considerable potential. From March 2021 to January 2022, a 1:15-scale prototype was tested in Reggio Calabria, Italy, which gave crucial insights into how these structures behave under different wave conditions. This study investigates the application of Artificial Neural Networks (ANNs) to predict changes in mooring loads, particularly at key points of the structure. By analyzing metocean data, several ANN models and optimization techniques were evaluated to identify the most accurate predictive model. With a Normalized Root Mean Square Error (NRMSE) of 1.7–4.7%, the results show how ANNs can effectively predict offshore platform dynamics. This research highlights the potential of machine learning in developing and managing sustainable ocean systems, setting the stage for future advancements in data-driven marine resource management.https://www.mdpi.com/2077-1312/12/11/2001artificial neural networksoffshore platformsaquaculture platformsmooring loadsrenewable energy |
| spellingShingle | Nikolas Martzikos Carlo Ruzzo Giovanni Malara Vincenzo Fiamma Felice Arena Applying Neural Networks to Predict Offshore Platform Dynamics Journal of Marine Science and Engineering artificial neural networks offshore platforms aquaculture platforms mooring loads renewable energy |
| title | Applying Neural Networks to Predict Offshore Platform Dynamics |
| title_full | Applying Neural Networks to Predict Offshore Platform Dynamics |
| title_fullStr | Applying Neural Networks to Predict Offshore Platform Dynamics |
| title_full_unstemmed | Applying Neural Networks to Predict Offshore Platform Dynamics |
| title_short | Applying Neural Networks to Predict Offshore Platform Dynamics |
| title_sort | applying neural networks to predict offshore platform dynamics |
| topic | artificial neural networks offshore platforms aquaculture platforms mooring loads renewable energy |
| url | https://www.mdpi.com/2077-1312/12/11/2001 |
| work_keys_str_mv | AT nikolasmartzikos applyingneuralnetworkstopredictoffshoreplatformdynamics AT carloruzzo applyingneuralnetworkstopredictoffshoreplatformdynamics AT giovannimalara applyingneuralnetworkstopredictoffshoreplatformdynamics AT vincenzofiamma applyingneuralnetworkstopredictoffshoreplatformdynamics AT felicearena applyingneuralnetworkstopredictoffshoreplatformdynamics |