Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network SCIE SCOPUS

Cited 1 time in WEB OF SCIENCE Cited 1 time in Scopus
Title
Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network
Author(s)
Yun, Miyoung; Kim, Jinah; Do, Kideok
KIOST Author(s)
Kim, Jinah(김진아)
Alternative Author(s)
김진아
Publication Year
2022-01
Abstract
Estimating wave-breaking indexes such as wave height and water depth is essential to understanding the location and scale of the breaking wave. Therefore, numerous wave-flume laboratory experiments have been conducted to develop empirical wave-breaking formulas. However, the nonlinearity between the parameters has not been fully incorporated into the empirical equations. Thus, this study proposes a multilayer neural network utilizing the nonlinear activation function and backpropagation to extract nonlinear relationships. Existing laboratory experiment data for the monochromatic regular wave are used to train the proposed network. Specifically, the bottom slope, deep-water wave height and wave period are plugged in as the input values that simultaneously estimate the breaking-wave height and wave-breaking location. Typical empirical equations employ deep-water wave height and length as input variables to predict the breaking-wave height and water depth. A newly proposed model directly utilizes breaking-wave height and water depth without nondimensionalization. Thus, the applicability can be significantly improved. The estimated wave-breaking index is statistically verified using the bias, root-mean-square errors, and Pearson correlation coefficient. The performance of the proposed model is better than existing breaking-wave-index formulas as well as having robust applicability to laboratory experiment conditions, such as wave condition, bottom slope, and experimental scale. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
ISSN
2077-1312
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/42253
DOI
10.3390/jmse10010050
Bibliographic Citation
Journal of Marine Science and Engineering, v.10, no.1, 2022
Publisher
MDPI
Keywords
Breaking-wave height; Machine learning; Multilayer neural network; Nonlinear relationships; Wave breaking; Breaking-water depth
Type
Article
Language
English
Document Type
Article
Publisher
MDPI
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