Review on Applications of Machine Learning in Coastal and Ocean Engineering

• Published in: Han-guk haeyang gonghak hoeji (Online) Vol. 36; no. 3; pp. 194 - 210
• Main Authors: Kim, Taeyoon, Lee, Woo-Dong
• Format: Journal Article
• Language: English
• Published: 한국해양공학회 01.06.2022; The Korean Society of Ocean Engineers
• Subjects: coastal engineering; data-driven model; machine learning; prediction; sensitivity analysis; 해양공학; Coastal engineering; Sensitivity analysis; Data-driven model; Machine learning; Prediction
• ISSN: 1225-0767; 2287-6715
• DOI: 10.26748/KSOE.2022.007

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.