A machine learning framework to forecast wave conditions

Coastal Engineering

Volumn 137, Issue , 2018, Pages 1-10

  James, Scott C ^a   Zhang, Yushan ^b   O'Donncha, Fearghal ^c  

^a BAYLOR UNIVERSITY   (United States)

^b University of Notre Dame   (United States)

^c IBM RESEARCH   (Ireland)

Author keywords

Machine learning; SWAN wave modeling; Wave condition forecasting

Indexed keywords

FORECASTING; LEARNING ALGORITHMS; MEAN SQUARE ERROR; OCEANOGRAPHY; WATER WAVES;

DATA SET; LEARNING FRAMEWORKS; MACHINE LEARNING MODELS; MACHINE-LEARNING; SWAN WAVE MODELING; TEST DATA; WAVE CONDITIONS; WAVE HEIGHTS; WAVE MODELLING; WAVE-CONDITION FORECASTING;

MACHINE LEARNING;

CONCEPTUAL FRAMEWORK; DATA SET; MACHINE LEARNING; OCEAN WAVE; WAVE MODELING;

CALIFORNIA; MONTEREY BAY; UNITED STATES;

EID: 85043507654     PISSN: 03783839     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.coastaleng.2018.03.004     Document Type: Article

References (44)

1. Bengio, Y., Grandvalet, Y., No unbiased estimator of the variance of k-fold cross-validation. J. Mach. Learn. Res. 5 (2004), 1089–1105.
2. Bidlot, J.-R., Holmes, D.J., Wittmann, P.A., Lalbeharry, R., Chen, H.S., Intercomparison of the performance of operational ocean wave forecasting systems with buoy data. Weather Forecast. 17 (2002), 287–310.
3. Bieker, H.P., Slupphaug, O., Johansen, T.A., Real-time production optimization of oil and gas production systems: A technology survey. SPE Prod. Oper. 22 (2007), 382–391.
4. Blanning, R.W., The construction and implementation of metamodels. Simulation 24 (1975), 177–184.
5. Browne, M., Strauss, D., Castelle, B., Blumenstein, M., Tomlinson, R., Lane, C., Empirical estimation of nearshore waves from a global deep-water wave model. Geosci. Rem. Sens. Lett. IEEE 3 (2006), 462–466.
6. Browne, M., Castelle, B., Strauss, D., Tomlinson, R., Blumenstein, M., Lane, C., Near-shore swell estimation from a global wind-wave model: Spectral process, linear, and artificial neural network models. Coast Eng. 54 (2007), 445–460.
7. nd Marine Energy Technology Symposium, 2014, 1–10.
8. Cesa-Bianchi, N., Conconi, A., Gentile, C., On the generalization ability of on-line learning algorithms. IEEE Trans. Inf. Theor. 50 (2004), 2050–2057.
9. Chang, G., Ruehl, K., Jones, C., Roberts, J.D., Chartrand, C.C., Numerical modeling of the effects of wave energy converter characteristics on nearshore wave conditions. Renew. Energy 89 (2016), 636–648.
10. DeVries, P.M., Thompson, T.B., Meade, B.J., Enabling large-scale viscoelastic calculations via neural network acceleration. Geophys. Res. Lett. 44 (2017), 2662–2669.
11. Dushaw, B.D., Egbert, G.D., Worcester, P.F., Cornuelle, B.D., Howe, B.M., Metzger, K., A TOPEX/POSEIDON global tidal model (TPXO.2) and barotropic tidal currents determined from long-range acoustic transmissions. Prog. Oceanogr. 40 (1997), 337–367.
12. EPRI, Mapping and Assessment of the United States Ocean Wave Energy Resource. Technical Report 1024637, Palo Alto, CA, 2011.
13. Etemad-Shahidi, A., Mahjoobi, J., Comparison between M5’ model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Eng. 36 (2009), 1175–1181.
14. FAO, The State of the World Fisheries and Aquaculture 2016. Contributing to the Food Security and Nutrition for All. Technical Report, 2016, Food and Agriculture Organization of the United Nations.
15. Goodfellow, I., Bengio, Y., Courville, A., Deep Learning. 2016, MIT Press.
16. Haas, K., Ahn, S., Neary, V.S., Bredin, S., Development of a wave energy resource classification system. Waterpower Week, 2017, 1–5.
17. Hodur, R.M., The Naval Research Laboratory's coupled ocean/atmosphere mesoscale prediction system (COAMPS). Mon. Weather Rev. 125 (1997), 1414–1430.
18. Integrated Ocean Observing System, Central and Northern California Ocean Observing System. 2017, CeNCOOS.
19. Kingma, D.P., Ba, J., Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980, 2014.
20. Kleijnen, J.P., Kriging metamodeling in simulation: A review. Eur. J. Oper. Res. 192 (2009), 707–716.
21. Knerr, S., Personnaz, L., Dreyfus, G., Single-layer learning revisited: A stepwise procedure for building and training a neural network. Neurocomp. NATO ASI Ser. (Ser. F: Comp. Syst. Sci.) 68 (1990), 41–50.
22. Komen, G.J., Cavaleri, L., Donelan, M., Dynamics and Modelling of Ocean Waves. 1996, Cambridge University Press.
23. LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521 (2015), 436–444.
24. Mahjoobi, J., Etemad-Shahidi, A., An alternative approach for the prediction of significant wave heights based on classification and regression trees. Appl. Ocean Res. 30 (2008), 172–177.
25. Makarynskyy, O., Improving wave predictions with artificial neural networks. Ocean Eng. 31 (2004), 709–724.
26. Mallet, V., Stoltz, G., Mauricette, B., Ozone ensemble forecast with machine learning algorithms. J. Geophys. Res.: Atmos., 114, 2009.
27. Mei, C.C., Stiassnie, M., Yue, D.K.-P., Theory and Applications of Ocean Surface Waves: Part 1: Linear Aspects. Part 2: Nonlinear Aspects. 1989, World Scientific.
28. 2 regularization for multiclass hinge loss models. Symposium on Machine Learning in Speech and Language Processing, 2011, 1–5.
29. th International Conference on Machine Learning, 2010.
30. Ocean Energy Systems, Annual Report: Ocean Energy Systems 2016. Technical Report 1024637, 2016.
31. O'Donncha, F., Zhang, Y., James, S.C., An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts. J. Mar. Syst., 2018.
32. O'Hagan, A., Bayesian analysis of computer code outputs: A tutorial. Reliab. Eng. Syst. Saf. 91 (2006), 1290–1300.
33. O'Reilly, W., Olfe, C.B., Thomas, J., Seymour, R., Guza, R., The California coastal wave monitoring and prediction system. Coast Eng. 116 (2016), 118–132.
34. Patterson, J., Thomas, J., Rosenfeld, L., Newton, J., Hazard, L., Scianna, J., Kudela, R., Mayorga, E., Cohen, C., Cook, M., et al. Addressing Ocean and Coastal Issues at the West Coast Scale through Regional Ocean Observing System Collaboration. MTS/IEEE OCEANS'12, 2012, 1–8.
35. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. SciKit-learn: Machine learning in Python. J. Mach. Learn. Res. 12 (2011), 2825–2830.
36. Peres, D., Iuppa, C., Cavallaro, L., Cancelliere, A., Foti, E., Significant wave height record extension by neural networks and reanalysis wind data. Ocean Model. 94 (2015), 128–140.
37. Razavi, S., Tolson, B.A., Burn, D.H., Review of surrogate modeling in water resources. Water Resour. Res., 48, 2012 W07401.
38. Schmidhuber, J., Deep learning in neural networks: an overview. Neural Network. 61 (2015), 85–117.
39. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L., Edge computing: vision and challenges. IEEE InternetThings J. 3 (2016), 637–646.
40. Song, Y., Haidvogel, D., A semi-implicit ocean circulation model using a generalized topography-following coordinate system. J. Comput. Phys. 115 (1994), 228–244.
41. The SWAN Team, SWAN Scientific and Technical Documentation. Technical Report SWAN Cycle III version 40.51, 2006, Delft University of Technology.
42. The Weather Company, 2017, The Weather Company.
43. TM version 3.14, Technical note. MMAB Contrib., 276, 2009, 220.
44. US DOE, Energy Department Announces Investment in Wave Energy Test Facility. 2016.