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http://dx.doi.org/10.9765/KSCOE.2022.34.6.315

Estimation of the Input Wave Height of the Wave Generator for Regular Waves by Using Artificial Neural Networks and Gaussian Process Regression  

Jung-Eun, Oh (Maritime ICT R&D Center, Korea Institute of Ocean Science and Technology)
Sang-Ho, Oh (Department of Civil Engineering, School of Smart and Green Technology, Changwon National University)
Publication Information
Journal of Korean Society of Coastal and Ocean Engineers / v.34, no.6, 2022 , pp. 315-324 More about this Journal
Abstract
The experimental data obtained in a wave flume were analyzed using machine learning techniques to establish a model that predicts the input wave height of the wavemaker based on the waves that have experienced wave shoaling and to verify the performance of the established model. For this purpose, artificial neural network (NN), the most representative machine learning technique, and Gaussian process regression (GPR), one of the non-parametric regression analysis methods, were applied respectively. Then, the predictive performance of the two models was compared. The analysis was performed independently for the case of using all the data at once and for the case by classifying the data with a criterion related to the occurrence of wave breaking. When the data were not classified, the error between the input wave height at the wavemaker and the measured value was relatively large for both the NN and GPR models. On the other hand, if the data were divided into non-breaking and breaking conditions, the accuracy of predicting the input wave height was greatly improved. Among the two models, the overall performance of the GPR model was better than that of the NN model.
Keywords
machine learning; artificial neural network; gaussian process regression; wave generation; physical experiment;
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Times Cited By KSCI : 5  (Citation Analysis)
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