Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
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Comparison of Wave Prediction and Performance Evaluation in Korea Waters based on Machine Learning

  • Heung Jin Park (Department of Ocean System Engineering, Jeju National University) ;
  • Youn Joung Kang (Department of Ocean System Engineering, Jeju National University)
  • Received : 2023.12.01
  • Accepted : 2024.02.08
  • Published : 2024.02.28
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Abstract

Waves are a complex phenomenon in marine and coastal areas, and accurate wave prediction is essential for the safety and resource management of ships at sea. In this study, three types of machine learning techniques specialized in nonlinear data processing were used to predict the waves of Korea waters. An optimized algorithm for each area is presented for performance evaluation and comparison. The optimal parameters were determined by varying the window size, and the performance was evaluated by comparing the mean absolute error (MAE). All the models showed good results when the window size was 4 or 7 d, with the gated recurrent unit (GRU) performing well in all waters. The MAE results were within 0.161 m to 0.051 m for significant wave heights and 0.491 s to 0.272 s for periods. In addition, the GRU showed higher prediction accuracy for certain data with waves greater than 3 m or 8 s, which is likely due to the number of training parameters. When conducting marine and offshore research at new locations, the results presented in this study can help ensure safety and improve work efficiency. If additional wave-related data are obtained, more accurate wave predictions will be possible.

Keywords

Acknowledgement

This study was supported by the research grant of Jeju National University in 2022.

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