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http://dx.doi.org/10.9765/KSCOE.2021.33.6.367

Wave Prediction in a Harbour using Deep Learning with Offshore Data  

Lee, Geun Se (Research Engineer, R&D Institute of DY Engineering Co., Ship &Ocean R&D Institute of DSME Co.)
Jeong, Dong Hyeon (R&D Institute of DY Engineering Co.)
Moon, Yong Ho (R&D Institute of DY Engineering Co.)
Park, Won Kyung (R&D Institute of DY Engineering Co.)
Chae, Jang Won (R&D Institute of DY Engineering Co.)
Publication Information
Journal of Korean Society of Coastal and Ocean Engineers / v.33, no.6, 2021 , pp. 367-373 More about this Journal
Abstract
In this study, deep learning model was set up to predict the wave heights inside a harbour. Various machine learning techniques were applied to the model in consideration of the transformation characteristics of offshore waves while propagating into the harbour. Pohang New Port was selected for model application, which had a serious problem of unloading due to swell and has lots of available wave data. Wave height, wave period, and wave direction at offshore sites and wave heights inside the harbour were used for the model input and output, respectively, and then the model was trained using deep learning method. By considering the correlation between the time series wave data of offshore and inside the harbour, the data set was separated into prevailing wave directions as a pre-processing method. As a result, It was confirmed that accuracy and stability of the model prediction are considerably increased.
Keywords
deep learning; machine learning; wave prediction; pre-processing;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 James, S.C., Zhang, Y. and O'Donncha, F. (2018). A machine learning framework to forecast wave conditions. Coastal Engineering, 137, 1-10.   DOI
2 Aurelien, G. (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow (2nd edition), ISBN 978-1-492-03264-9, 351 p.
3 Jeong, W.-M., Ryu, K.-H., Baek, W.D. and Choi, H.J. (2011). Downtime analysis for Pohang New Harbor through long-term investigation of waves and winds. Journal of Korean Society of Coastal and Ocean Engineers, 23(3), 226-234.   DOI
4 Kim, T. (2020). A study on the prediction technique for wind and wave using deep learning. Journal of the Korean Society for Marine Environment & Energy, 23(3), 142-147.   DOI
5 LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.   DOI
6 Lee, K.-H., Kim, T.-G. and Kim, D.-S. (2020). Prediction of wave breaking using machine learning open source platform. Journal of Korean Society of Coastal and Ocean Engineers, 32(4), 262-272.   DOI
7 Ministry of Oceans and Fisheries. (2019). Nationwide Deep Sea Design Wave Estimation Report.
8 Oh, J.E., Suh, K.D., Oh, S.H. and Jeon, W.M. (2016). Estimation of infragravity waves inside Pohang New Port. Journal of Coastal Research, Special Issue No. 75, 432-436.
9 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thrion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). SciKit-learn: Machine learning in Python. J. Mach. Learn, Res. 12, 2815-2830.
10 Pohang Regional Office of Oceans and Port. (2012). Feasibility Study for Swell Improvement Measures and Basic Plans Report of Pohang New Port.
11 Jeong, W.-M., Oh, S.-H., Ryu, K.-H., Back, J.-D. and Choi, I.-H. (2018). Establishment of Wave Information Network of Korea (WINK). Journal of Korean Society of Coastal and Ocean Engineers, 30(6), 326-336.   DOI
12 Park, S.B., Shin, S.Y., Jung, K.H. and Lee, B.G. (2021). Prediction of significant wave height in Korea strait using machine learning. Journal of Ocean Engineering and Technology, 35(5), 336-346.   DOI
13 Park, W.K., Jeong, W.M., Moon, Y.H., Ryu, K.H., Baek, W.D., Jin, J.Y. and Chae, J.W. (2014). Boussinesq modeling of infragravity waves in Pohang New Harbor induced by directional short waves. presented at ICCE2014 Seoul.
14 Suh, K.-D., Lee, A., Lee, J.-S., Kim, I.-C. and Lee, S.B. (2019). Engineer-Friendly Machine Learning Models for Coastal Structure Design pp. 41-71.