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DOI: https://doi.org/10.26748/KSOE.2021.021    [Articles in press]

Prediction of Significant Wave Height in Korea Strait Using Machine Learning
Sung Boo Park1  , Seong Yun Shin1  , Kwang Hyo Jung2  , Byung Gook Lee3 
1Graduate student, Department of Naval Architecture and Ocean Engineering, Pusan National University, Busan, Korea
2Professor, Department of Naval Architecture and Ocean Engineering, Pusan National University, Busan, Korea
3Professor, Department of Computer Engineering, Dongseo University, Busan, Korea
Corresponding author:  Kwang Hyo Jung, Tel: +82-51-510-2343, kjung@pusan.ac.kr
Received: 7 May 2021   • Revised: 8 July 2021   • Accepted: 9 July 2021
Copyright © The Korean Society of Ocean Engineers     Open access / Under a Creative Commons License
Key Words: Machine learning, Significant wave height, Korea Strait, Feedforward neural network, Long short-term memory, Pearson correlation coefficient
Abstract
The prediction of wave conditions is crucial in the field of marine and ocean engineering. Hence, this study aims to predict the significant wave height through machine learning (ML), a soft computing method. The adopted metocean data, collected from 2012 to 2020, were obtained from the Korea Institute of Ocean Science and Technology. We adopted the feedforward neural network (FNN) and long-short term memory (LSTM) models to predict significant wave height. Input parameters for the input layer were selected by Pearson correlation coefficients. To obtain the optimized hyperparameter, we conducted a sensitivity study on the window size, node, layer, and activation function. Finally, the significant wave height was predicted using the FNN and LSTM models, by varying the three input parameters and three window sizes. Accordingly, FNN (W48) (i.e., FNN with window size 48) and LSTM (W48) (i.e., LSTM with window size 48) were superior outcomes. The most suitable model for predicting the significant wave height was FNN(W48) owing to its accuracy and calculation time. If the metocean data were further accumulated, the accuracy of the ML model would have improved, and it will be beneficial to predict added resistance by waves when conducting a sea trial test.


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