Comparison of Wave Prediction and Performance Evaluation in Korea Waters based on Machine Learning
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초록
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.
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.
참고문헌 (24)
- Adytia, D., "Wind wave prediction by using autoregressive integrated moving average model : case study in Jakarta bay", International Journal on Information and Communication Technology(IJoICT), 4(2), pp.33~42 (2018)[DOI]
- Carter, D. J. T., "Prediction of wave height and period for a constant wind velocity using the JONSWAP results", Ocean Engineering, 9(1), pp.17~33 (1982)[DOI]
- "Empirical evaluation of gated recurrent neural networks on sequence modeling/arXiv:1412.3555." (2014)[URL]
- Domala, V., "Wave data prediction with optimized machine learning and deep learning techniques", Journal of Computational Design and Engineering, 9(3), pp.1107~1122 (2022)[DOI]
- Froehlich, H. E., "Offshore aquaculture : I know it when I see it", Frontiers in Marine Science, 4, pp.154~ (2017)[DOI]
- Gers, F. A., "Learning to forget : Continual prediction with LSTM", Neural computation, 12(10), pp.2451~2471 (2000)[DOI]
- Group, S., "A shallow water intercomparison of three numerical wave prediction models(SWIM)", Quarterly Journal of the Royal Meteorological Society, 111(470), pp.1087~1112 (1985)[DOI]
- Group, T. W., "The WAM model―A third generation ocean wave prediction model", Journal of Physical Oceanography, 18(12), pp.1775~1810 (1988)[DOI]
- Hochreiter, S., "Long short-term memory", Neural computation, 9(8), pp.1735~1780 (1997)[DOI]
- Hu, H., "Predicting Lake Erie wave heights and periods using XGBoost and LSTM", Ocean Modelling, 164, pp.101832~ (2021)[DOI]
- Kang, B. H., "A novel method for long-term time series analysis of significant wave height", 2016Techno-Ocean (Techno-Ocean), pp.478~484 (2016)
- Kim, J., "STG-OceanWaveNet : Spatio-temporal geographic information guided ocean wave prediction network", Ocean Engineering, 257, pp.111576~ (2022)[DOI]
- Kumar, N. K., "Ocean wave height prediction using ensemble of extreme learning machine", Neurocomputing, 277, pp.12~20 (2018)[DOI]
- Masini, R. P., "Machine learning advances for time series forecasting", Journal of Economic Surveys, 37(1), pp.76~111 (2023)[DOI]
- "Status of marine accidents by accident type" (2023)[URL]
- Minuzzi, F. C., "A deep learning approach to predict significant wave height using long short-term memory", Ocean Modelling, 181, pp.102151~ (2023)[DOI]
- Park, S. B., "Prediction of significant wave height in Korea strait using machine learning", Journal of Ocean Engineering and Technology, 35(5), pp.336~346 (2021)[DOI]
- Pushpam, M. M., "Forecasting significant wave height using RNN-LSTM models", 2020 4th International Conference on Intelligent Computing and Control Systems, pp.1141~1146 (2020)
- Rhines, P., "Topographic Rossby waves in a rough-bottomed ocean", Journal of Fluid Mechanics, 61(3), pp.583~607 (1973)[DOI]
- Sabatier, F, "US Army Corps of Engineers, Coastal Engineering Manual (CEM), Engineer Manual 1110-2-1100. US Army Corps of Engineers, Washington, DC (6 volumes)", Journal of Mediterranean geography, (108), pp.146~ (2007)[DOI]
- Son, B., "Optimization of SWAN wave model to improve the accuracy of winter storm wave prediction in the East Sea", Journal of Ocean Engineering and Technology, 35(4), pp.273~286 (2021)
- Swamp Group, "Ocean wave modeling" (1985), Springer Science &Business Media
- Tolman, H. L., "User manual and system documentation of WAVEWATCH III TM version 3.14", Technical note, MMAB Contribution, 276(220) (2009)
- You, S. H., "Research on wind waves characteristics by comparison of regional wind wave prediction system and ocean buoy data", Journal of Ocean Engineering and Technology, 24(6), pp.7~15 (2010)