Machine learning approach to predict significant wave height
DOI:
https://doi.org/10.26577/JMMCS.2021.v110.i2.08Keywords:
Machine learning, significant wave height, Support vector regressionAbstract
To estimate significant wave height of ocean wave, a machine learning framework is developed. Significant wave height and period can be used by supervised training of machine learning to predict ocean conditions. In this paper we proposed a method to predict significant wave height using Support vector regression (SVR). Buoy dataset taken from the Queensland government open data portal the input from which were aggregated into supervised learning test and training data sets, which were supplied to machine learning models. The SVR model replicated significant wave height with a root-mean-squared-error of 0.044 and performed on the test data with 95% accuracy. Comparing to forecasting with the physics-based model the Machine learning SVR model requires only a fraction (< 1=1200th) of the computation time, to predict Significant wave height.
Key words: Machine learning, significant wave height, Support vector regression.
References
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[7] V. Mallet, G. Stoltz, B. Mauricette, "Ozone ensemble forecast with machine learning algorithms" , Journal of Geophysical Research: Atmospheres 114 (2009).
[8] D. Peres, C. Iuppa, L. Cavallaro, A. Cancelliere, E. Foti, "Significant wave height record extension by neural networks and reanalysis wind data" , Ocean Modelling 94 (2015): 128-140.
[9] O. Makarynskyy, "Improving wave predictions with artificial neural networks" , Ocean Engineering 31 (2004): 709-724.
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[11] J. Mahjoobi, A. Etemad-Shahidi, "An alternative approach for the prediction of significant wave heights based on classification and regression trees" , Applied Ocean Research 30 (2008): 172-177.
[12] M. Browne, D. Strauss, B. Castelle, M. Blumenstein, R. Tomlinson, C. Lane, "Empirical estimation of nearshore waves from a global deep-water wave model" , IEEE Geoscience and Remote Sensing Letters 3 (2006): 462-466.
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[14] The SWAN Team, SWAN Scientific and Technical Documentation Technical Report SWAN Cycle III version 40.51, Delft University of Technology (2006).
[15] G. J. Komen, L. Cavaleri, M. Donelan, "Dynamics and Modelling of Ocean Waves" , Cambridge University Press (1996).
[16] C. C. Mei, M. Stiassnie, D. K.-P. Yue, "Theory and Applications of Ocean Surface Waves: Part 1: Linear Aspects. Part 2: Nonlinear Aspects" , World Scientific (1989).
[17] Y. Song, D. Haidvogel, "A semi-implicit ocean circulation model using a generalized topography-following coordinate system" , Journal of Computational Physics 115 (1994): 228-244.
[18] J. Patterson, J. Thomas, L. Rosenfeld, J. Newton, L. Hazard, J. Scianna, R. Kudela, E. Mayorga, C. Cohen, M. Cook, et al., "Addressing ocean and coastal issues at the west coast scale through regional ocean observing system collaboration,
in: Oceans’12" , IEEE 1-8.
[19] The Weather Company The Weather Company (2017).
[20] G. Chang, K. Ruehl, C. Jones, C. C. Roberts, J. D.and Chartrand, "Numerical modeling of the effects of wave energy converter characteristics on nearshore wave conditions" , Renewable Energy 89 (2016): 636-648.
[21] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al., "SciKit-Learn: Machine learning in Python" , Journal of Machine Learning Research 12 (2011):
2825-2830.
[22] Scott C James, Yushan Zhang, Fearghal O’Donncha, "See discussions, A Machine Learning Framework to Forecast Wave Conditions" , Coastal Engineering (2017): 46556-5637.
[23] N. Cesa-Bianchi, A. Conconi, C. Gentile, "On the generalization ability of on-line learning algorithms" , IEEE Transactions on Information Theory 50 (2004): 2050-2057.
[24] Queensland Government Coastal Data System - Near real time wave data, open data portal https://www.data.qld.gov.au/dataset/coastal-data-system-near-real-time-wave-data
[25] Vapnik, V. The Nature of Statistical Learning Theory (Springer, New York, 1995).
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Published
2021-09-27
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Section
Computer Science
