Review on Applications of Machine Learning in Coastal and Ocean Engineering
Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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The Korean Society of Ocean Engineers
2022-06-01
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| Series: | 한국해양공학회지 |
| Subjects: | |
| Online Access: | https://doi.org/10.26748/KSOE.2022.007 |
| _version_ | 1811329951768510464 |
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| author | Taeyoon Kim Woo-Dong Lee |
| author_facet | Taeyoon Kim Woo-Dong Lee |
| author_sort | Taeyoon Kim |
| collection | DOAJ |
| description | Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered. |
| first_indexed | 2024-04-13T15:53:03Z |
| format | Article |
| id | doaj.art-4f8c4888710849468d8d83a83734f19b |
| institution | Directory Open Access Journal |
| issn | 1225-0767 2287-6715 |
| language | English |
| last_indexed | 2024-04-13T15:53:03Z |
| publishDate | 2022-06-01 |
| publisher | The Korean Society of Ocean Engineers |
| record_format | Article |
| series | 한국해양공학회지 |
| spelling | doaj.art-4f8c4888710849468d8d83a83734f19b2022-12-22T02:40:47ZengThe Korean Society of Ocean Engineers한국해양공학회지1225-07672287-67152022-06-0136319121010.26748/KSOE.2022.007Review on Applications of Machine Learning in Coastal and Ocean EngineeringTaeyoon Kim0https://orcid.org/0000-0002-5060-5302Woo-Dong Lee1https://orcid.org/0000-0001-7776-4664Gyeongsang National UniversityGyeongsang National UniversityRecently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.https://doi.org/10.26748/KSOE.2022.007machine learningdata-driven modelcoastal engineeringpredictionsensitivity analysis |
| spellingShingle | Taeyoon Kim Woo-Dong Lee Review on Applications of Machine Learning in Coastal and Ocean Engineering 한국해양공학회지 machine learning data-driven model coastal engineering prediction sensitivity analysis |
| title | Review on Applications of Machine Learning in Coastal and Ocean Engineering |
| title_full | Review on Applications of Machine Learning in Coastal and Ocean Engineering |
| title_fullStr | Review on Applications of Machine Learning in Coastal and Ocean Engineering |
| title_full_unstemmed | Review on Applications of Machine Learning in Coastal and Ocean Engineering |
| title_short | Review on Applications of Machine Learning in Coastal and Ocean Engineering |
| title_sort | review on applications of machine learning in coastal and ocean engineering |
| topic | machine learning data-driven model coastal engineering prediction sensitivity analysis |
| url | https://doi.org/10.26748/KSOE.2022.007 |
| work_keys_str_mv | AT taeyoonkim reviewonapplicationsofmachinelearningincoastalandoceanengineering AT woodonglee reviewonapplicationsofmachinelearningincoastalandoceanengineering |