메뉴 건너뛰기
Library Notice
Institutional Access
If you certify, you can access the articles for free.
Check out your institutions.
ex)Hankuk University, Nuri Motors
Log in Register Help KOR
Subject
Humanities
General Humanities History Philosophy Religious Studies Linguistics and Literature Other Humanities
Social science
General Social Science Business Management Tourism Education Military Science Library and Information Science Law Sociology Journalism and Media Studies Psychological Science Political Science and Diplomacy Geography Public Administration Economics International Trade Anthropology Other Social Science
Natural science
General Natural Science Physics Biology Life Sciences Mathematics/Statistics Astronomy Earth Science Chemistry Other Natural Science
Engineering
General Engineering Architectural Engineering Mechanical Engineering Industrial Engineering Material/Energy Engineering Electrical and Electronic Engineering Marine and Ocean Engineering Chemical Engineering Computer Science Other Engineering
Medical science
General Medical Science Nursing Science Veterinary Pharmacy Traditional Korean Medicine Basic Medicine Clinical Medicine Other Medicine
Agriculture and Fishery
General Agriculture and Fishery Agriculture Fishery Science Food Science Forestry Animal Husbandry Marine Transportation Studies Other Agriculture and Fishery
Art and Kinesiology
General Arts Design Fine Arts Cosmetology Photography Music Fashion Kinesiology Performing Media Arts Other Arts and Kinesiology
Interdisciplinary Studies
Interdisciplinary Research Science and Technology Studies Brain and Cognitive Science Technology Policy Other Interdisciplinary Studies

KOR

Review on Applications of Machine Learning in Coastal and Ocean Engineering
Recommendations
Search
Questions

논문 기본 정보

Type
Academic journal
Author
Taeyoon Kim (Gyeongsang National University) Woo-Dong Lee (Gyeongsang National University)
Journal
The Korean Society of Ocean Engineers Journal of Ocean Engineering and Technology Vol.36 No.3(Wn.166) KCI Excellent Accredited Journal SCOPUS
Published
2022.6
Pages
194 - 210 (17page)

Usage

cover
📌
Topic
📖
Background
🔬
Method
🏆
Result
Review on Applications of Machine Learning in Coastal and Ocean Engineering
Ask AI
Recommendations
Search
Questions

Abstract· Keywords

Report Errors
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.
#Machine learning #Data-driven model #Coastal engineering #Prediction #Sensitivity analysis

Contents

ABSTRACT
1. Introduction
2. Machine Learning Model
3. Application of ML in Coastal and Marine Engineering Field
4. Conclusions
References

References (58)

Add References
Balas, C.E., Koc, L., & Balas, L. (2004). Predictions of Missing Wave Data by Recurrent Neuronets. Journal of Waterway, Port, Coastal, and Ocean Engineering, 130(5), 256–265. https://doi.org/10.1061/(ASCE)0733-950X(2004)130:5(256) Crossref Boser, B.E., Guyon, I., & Vapnik, V.N. (1992). A Training Algorithm for Optimal Margin Classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 5, 144–152. Pittsburgh, ACM. https://doi.org/10.1145/130385.130401 Crossref Chen, J., Pillai, A.C., Johanning, L., & Ashton, I. (2021). Using Machine Learning to Derive Spatial Wave Data: A Case Study for a Marine Energy Site. Environmental Modelling & Software, 142, 105066. https://doi.org/10.1016/j.envsoft.2021.105066 Crossref De Rouck, J., Van de Walle, B., & Geeraerts, J. (2004). Crest Level Assessment of Coastal Structures by Full Scale Monitoring, Neural Network Prediction and Hazard Analysis on Permissible Wave Overtoppingg - (CLASH). Proceedings of the EurOCEAN 2004 (European Conference on Marine Science & Ocean Technology), Galway, Ireland, EVK3-CT-2001-00058, 261–262. google schola Dwarakish, G.S., Rakshith, S., & Natesan, U. (2013). Review on Applications of Neural Network in Coastal Engineering. Artificial Intelligent Systems and Machine Learning, 5(7), 324–331. google schola

Recommendations

It is an article recommended by DBpia according to the article similarity. Check out the related articles!

Related Authors

Recently viewed articles

Structural bonding for civil engineering structures: New model of composite I-steel-concrete beam strengthened with CFRP plate
Bensatallah Tayeb, Tahar Hassaine Daouadji, Rabahi Abderezak 외 1명International Association of Structural Engineering And Mechanics2021.11
 A Publicity Broadcasting Model vs. A Public Broadcasting Model : Content Analysis of Economic News of KBS and BBC
Soo-Young Kim, Sung-Gwan ParkSBS2016.02

Comments(0)

0

Write first comments.