Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/10/1/50 |
| _version_ | 1797492928590905344 |
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| author | Miyoung Yun Jinah Kim Kideok Do |
| author_facet | Miyoung Yun Jinah Kim Kideok Do |
| author_sort | Miyoung Yun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:12:39Z |
| format | Article |
| id | doaj.art-dba9911a707e4459bfe1db45210c4eb1 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T01:12:39Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-dba9911a707e4459bfe1db45210c4eb12023-11-23T14:16:11ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-01-011015010.3390/jmse10010050Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural NetworkMiyoung Yun0Jinah Kim1Kideok Do2Department of Convergence Study on the Ocean Science and Technology, Korea Maritime & Ocean University, Busan 49112, KoreaCoastal Disaster Research Center, Korea Institute of Ocean Science and Technology, Busan 49111, KoreaDepartment of Ocean Engineering, Korea Maritime & Ocean University, Busan 49112, KoreaEstimating 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.https://www.mdpi.com/2077-1312/10/1/50wave breakingbreaking-wave heightbreaking-water depthmultilayer neural networknonlinear relationshipsmachine learning |
| spellingShingle | Miyoung Yun Jinah Kim Kideok Do Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network Journal of Marine Science and Engineering wave breaking breaking-wave height breaking-water depth multilayer neural network nonlinear relationships machine learning |
| title | Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network |
| title_full | Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network |
| title_fullStr | Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network |
| title_full_unstemmed | Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network |
| title_short | Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network |
| title_sort | estimation of wave breaking index by learning nonlinear relation using multilayer neural network |
| topic | wave breaking breaking-wave height breaking-water depth multilayer neural network nonlinear relationships machine learning |
| url | https://www.mdpi.com/2077-1312/10/1/50 |
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