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Review on Applications of Machine Learning in Coastal and Ocean Engineeringopen accessReview on Applications of Machine Learning in Coastal and Ocean Engineering

Other Titles
Review on Applications of Machine Learning in Coastal and Ocean Engineering
Authors
김태윤이우동
Issue Date
2022
Publisher
한국해양공학회
Keywords
Machine learning; Data-driven model; Coastal engineering; Prediction; Sensitivity analysis
Citation
한국해양공학회지, v.36, no.3, pp.194 - 210
Indexed
KCI
Journal Title
한국해양공학회지
Volume
36
Number
3
Start Page
194
End Page
210
URI
https://scholarworks.bwise.kr/gnu/handle/sw.gnu/2254
DOI
10.26748/KSOE.2022.007
ISSN
1225-0767
Abstract
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.
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해양과학대학 (해양토목공학과)
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