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
- 김태윤; 이우동
- 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
- Pages
- 17
- Indexed
- KCI
- Journal Title
- 한국해양공학회지
- Volume
- 36
- Number
- 3
- Start Page
- 194
- End Page
- 210
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/2254
- DOI
- 10.26748/KSOE.2022.007
- ISSN
- 1225-0767
2287-6715
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 해양과학대학 > 해양토목공학과 > Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.gnu.ac.kr/handle/sw.gnu/2254)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.