Prediction of Significant Wave Height in Korea Strait Using Machine Learning

• Published in: Han-guk haeyang gonghak hoeji (Online) Vol. 35; no. 5; pp. 336 - 346
• Main Authors: Park, Sung Boo, Shin, Seong Yun, Jung, Kwang Hyo, Lee, Byung Gook
• Format: Journal Article
• Language: English
• Published: 한국해양공학회 01.10.2021; The Korean Society of Ocean Engineers
• Subjects: feedforward neural network; korea strait; long short-term memory; machine learning; pearson correlation coefficient; significant wave height; 해양공학; Long short-term memory; Significant wave height; Korea Strait; Feedforward neural network; Machine learning; Pearson correlation coefficient
• ISSN: 1225-0767; 2287-6715
• DOI: 10.26748/KSOE.2021.021

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.