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Journal of Korean Society of Coastal and Ocean Engineers > Volume 32(6); 2020 > Article
J Korean Soc Coast Ocean Eng 2020;32(6): 384-395. doi: https://doi.org/10.9765/KSCOE.2020.32.6.384
지도학습을 이용한 새로운 선형 쇄파지표식 개발
최병종1, 박창욱2, 조용환3, 김도삼4, 이광호5
1가톨릭관동대학교 대학원 에너지환경융합학과 대학원생
2(주) 오셔닉 대표
3일본 나고야대학교 토목환경공학과 조교수
4한국해양대학교 건설공학과 교수
5가톨릭관동대학교 토목공학과 부교수
A Proposal of New Breaker Index Formula Using Supervised Machine Learning
Byung-Jong Choi1, Chang-Wook Park2, Yong-Hwan Cho3, Do-Sam Kim4, Kwang-Ho Lee5
1Graduate Student, Dept. of Energy and Environmental Eng., Graduate School, Catholic Kwandong University
2CEO of OCEANIC C&T Co., Ltd.
3Assistant Professor, Dept. of Civil and Environmental Eng., Nagoya University
4Professor, Dept. of Civil Eng., Korea Maritime and Ocean University
5Associate Professor, Dept. of Civil Engineering, Catholic Kwandong University, 24, Beomil-ro 579, Gangneung-si, Gangwon-do 25601, Korea
Corresponding author: Kwang-Ho Lee ,Tel: +82-33-649-7637, Fax: +82-33-649-7639, Email: klee@cku.ac.kr
Received: September 16, 2020;  Revised: October 30, 2020.  Accepted: November 10, 2020.
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Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions, such as sediment transport, longshore currents, and shock wave pressure. Therefore, it is crucial to accurately predict breaker index such as breaking wave height and breaking depth, when designing coastal structures. Numerous scientific efforts have been made in the past by many researchers to identify and predict the breaking phenomenon. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. In this paper, we applied a representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a model for prediction of the breaking index is developed from previously published experimental data on the breaking wave, and a new linear equation for prediction of breaker index is presented from the trained model. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.
Keywords: wave breaking height, breaking depth, machine learning, supervised learning, breaker index formula
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