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Warianty tytułu
Języki publikacji
Abstrakty
This paper describes the application of methodology based on the artificial neural network technique to make short-term wave forecasts. The neural network model is used to predict significant wave height at a selected location on the Baltic Sea based on wave and/or wind data at ten points scattered on the sea. High quality hindcast data were used in the process of developing the forecast methodology. The data originated from the WAM4 wave model. The results show that the neural network technique allowed significant wave height to be predicted accurately. The agreement obtained by a comparison with a testing data set was sufficiently good to confirm the effectiveness of this approach.
Słowa kluczowe
Rocznik
Tom
Strony
183--201
Opis fizyczny
Bibliogr. 22 poz., il.
Twórcy
autor
- Institute of Hydro-Engineering of the Polish Academy of Science, ul. Kościerska 7, 80-328 Gdańsk, Poland, bep@ibwpan.gda.pl
Bibliografia
- 1. Booij N., Ris R. C., Holthuijsen L. H. (1999), A third-generation wave model for coastal regions, Journal of Geophysical Research, Vol. 104, No. C4, 7649–7666.
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- 3. Cieslikiewicz W., Paplinska-Swerpel B. (2005), Reconstruction of wind waves in the Baltic Sea in 1958–2001, Inzynieria Morska i Geotechnika, Vol. 26, No. 4, 313–321 (in Polish).
- 4. Cieslikiewicz W., Paplinska-Swerpel B., Soares C. G. (2004), Multi-decadal wind waves modelling over the Baltic Sea, 29th Intern. Conf. Coastal Engng, ICCE, 19–24 September, Lisbon, 778–790.
- 5. Demuth H., Beale M. (2001), Neural Network Toolbox, The MathWorks.
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- 7. Duch W., Korbicz J., Rutkowski L., Tadeusiewicz R. (2000), Biocybernetics and Biomedical Engineering, Vol. 6, Neural Networks, Warszawa, Akademicka Oficyna Wydawnicza EXIT (in Polish).
- 8. Engelbrecht A. P., Cloete I., Zurada J. M. (1995), Determining the significance of input parameters using sensitivity analysis, [in:] From Natural to Artificial Neural Computations, Eds. J. Mira, F. Sandoval, IWANN, Malaga, 382–388.
- 9. Feser F., Weisse R., von Storch H. (2001), Multi-decadal atmospheric modelling for Europe yields multi-purpose data, EoS, Vol. 82, No. 28, 305–310.
- 10. Hertz J., Krogh A., Palmer R. G. (1991), Introduction to the Theory of Neural Computation, Santa Fe Institute, Addison-Wesley Publishing Company.
- 11. Huang W., Murray C., Kraus N., Rosati J. (2003), Development of a regional neural network for coastal water level predictions, Ocean Engineering, Vol. 30, 2275–2295.
- 12. Jang J.-S. R., Sun C.-T., Mizutani E. (1997), Neuro-Fuzzy and Soft Computing, Prentice Hall.
- 13. Komen G. J., Cavaleri L., Donelan M., Hasselmann K., Hasselmann S., Janssen A. E. M. (1994), Dynamics and Modelling of Ocean Waves, Cambridge University Press.
- 14. Makarsky O., Pires-Silva A. A., Makarynska D., Ventura-Soares C. (2002), Artificial neural network in the forecasting of wave parameters, 7th International Workshop onWave Hindcasting and Forecasting Preprints, Banff-Alberta, Canada, October 21–25.
- 15. Massel S. R. (1996), Advanced Series on Ocean Engineering, Volume 11, Ocean Surface Waves: Their Physics and Prediction, World Scientific, Singapore.
- 16. Medina J. R., Serrano-Hidalgo O. (2005), Reconstruction of significant wave height time series using neural networks, Proc. of Fifth International Symposium on OceanWaveMeasurement and Analysis, WAVES 2005, Madrid, IAHR, 34, 1–10.
- 17. Nguyen D.,Widrow B. (1990), Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights, Proc. of the International Joint Conference on Neural Networks, Vol. 3, 21–26.
- 18. Nielsen J. W. (2003), Verification of DMI wave forecasts 2nd quarter of 2003, Technical report 03–28, The Danish Meteorological Institute, Copenhagen, Denmark.
- 19. Paplinska B. (1999), Wave analysis at Lubiatowo and in the Pomeranian Bay based on measurements from 1997/1998 – comparison with modelled data (WAM4 model), Oceanologia, 41(2), 241–254.
- 20. Soares C. G., Weisse R., Carretero J. C, Alvarez E. (2002), A 40-years hindcast of wind, sea level and waves in European waters, Proc. 21st International Conf. on Offshore Mechanics and Arctic Engng, OMAE, Oslo, Norway.
- 21. Sztobryn M. (2003), Forecast of storm surge by means of artificial neural network, J. Sea Res., Spec. Issue, 49(4), 317–322.
- 22. WAMDI group (1988), The WAM model – a third-generation ocean wave prediction model, Journal of Physics Oceanography, Vol. 18, 1775–1810.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT3-0039-0041