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http://dx.doi.org/10.9765/KSCOE.2020.32.6.384

A Proposal of New Breaker Index Formula Using Supervised Machine Learning  

Choi, Byung-Jong (Dept. of Energy and Environmental Eng., Graduate School, Catholic Kwandong University)
Park, Chang-Wook (OCEANIC C&T Co., Ltd.)
Cho, Yong-Hwan (Dept. of Civil and Environmental Eng., Nagoya University)
Kim, Do-Sam (Dept. of Civil Eng., Korea Maritime and Ocean University)
Lee, Kwang-Ho (Dept. of Civil Engineering, Catholic Kwandong University)
Publication Information
Journal of Korean Society of Coastal and Ocean Engineers / v.32, no.6, 2020 , pp. 384-395 More about this Journal
Abstract
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;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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