J Navig Port Res > Volume 48(6); 2024 > Article
Journal of Navigation and Port Research 2024;48(6):580-588.
DOI: https://doi.org/10.5394/KINPR.2024.48.6.580    Published online December 31, 2024.
한국 해역 관측 데이터를 이용한 앙상블 학습 기반 파고 예측
이원희2, 이한진1
1선박해양플랜트연구소 책임연구원
2선박해양플랜트연구소 선임연구원
Wave Height Prediction based on Ensemble Learning Using Observed Data from South Korea
Wonhee Lee2, Han Jin Lee1
1Principal Researcher, Korea Research Institute of Ships and Ocean Engineering, Daejon, Korea
2Senior Researcher, Korea Research Institute of Ships and Ocean Engineering, Daejon, Korea
Correspondence:  Wonhee Lee, Tel: 042)866-3718, 
Email: weelon@kriso.re.kr
Received: 9 September 2024   • Revised: 14 September 2024   • Accepted: 4 November 2024
Abstract
Wave height prediction is crucial in the marine sector and has wide-ranging applications, including determining the optimal route for autonomous ships, installing and operating wave power generators, and deploying offshore platforms. In the past, wave prediction was primarily based on numerical models grounded in physical principles; however, these methods require extensive computational resources. Recently, many researchers have applied machine learning methods to overcome these limitations. In this study, we utilized ensemble learning, a type of machine learning approach, to predict wave heights based on data collected from ocean observation stations in Korea. After determining the input variables of the model through correlation analysis, we applied and compared models using both bagging and boosting ensemble methods. The results indicated that, among the ensemble learning models, the boosting models outperformed the bagging models. This study is limited by its reliance on data from a single observation station. Therefore, future work will involve conducting research using data from multiple observation stations across various sea areas to validate the generalizability of the proposed model.
Key Words: wave height prediction, ensemble learning, correlation analysis, observed data based prediction, bagging, boosting
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