Investigation and Prediction of Caspian Sea Significant Wave Height Using Chaos Theory

Document Type : Research Article

Authors

1 Professor, Civil Engineering Faculty, University of Tabriz, Tabriz, Iran

2 Associate Professor, Department of Civil Engineering, Guilan University, Rasht, Iran

3 Associate Professor, Water Engineering Department, University of Tabriz, Tabriz, Iran

4 Ph.D. Candidate, Civil Engineering Faculty, University of Tabriz, Tabriz, Iran

Abstract

Significant wave height is mean of one third of the largest wave heights in a certain marine condition. Investigation and prediction of the significant wave height have been recently considered in marine system analysis including loadings over marine structures and sediment transport for designing, operation and marine researches. The capability of chaos theory in engineering particularly marine engineering has been gaining considerable interest in recent times. In this research, dynamic characteristics of the significant wave height time series in Caspian Sea at Anzali entrance are considered and the prediction has been performed using ideas gained from chaos theory. To reconstruct phase space, the time delay and embedding dimension are needed and for this purpose, autocorrelation function and algorithm of false nearest neighbors are used. Correlation dimension method is applied for investigating chaotic behavior of the significant wave height, which is the resultant of correlation dimensions, expresses chaotic behavior in the time series. Local prediction algorithm is used for time series prediction and results illustrate good and acceptable accuracy of chaos theory in quantitative prediction of seas significant wave height.

Keywords

References

[1] پری زنگنه،م.، عطائی، م.، معلم، پ.، " بازسازی فضای حالت سری های زمانی آشوبی با استفاده از یک روش هوشمند"، نشریه الکترونیک و قدرت دانشکده مهندسی برق. سال اول. ش 2، ص 3 تا 10، 1388.
[2]Elshorbagy, A.; Simonovic, S. P.; Panu, U.S.; “Estimation of missing stream flow data using  principles of chaos theory”, Journal of Hydrology, No. 255, pp. 123 – 133. , 2002.
[3] Fraser, A.; Swinney, H.L.; “Independent coordinates for strange attractors from mutual information”, Phys. Rev. A, No. 33, pp. 1134 –1140, 1986.
[4]Porporato, A.; Ridolfi, L.; “Nonlinear analysis of river flow time sequences”, Water Resources Research, No. 33(6), pp. 1353 -1367, 1997.
[5]Damle, C.; Yalcin, A.; “Flood Prediction Using Time Series Data Mining”, Journal of Hydrology, No. 333, pp. 305 – 316, 2007.
[6]Chakrabarti, S.K.,; Hydrodynamics of Offshore Structures, WIT Press, UK, 2001.
[7]Wu, J.; Lu, J.; Wang, J.; “Application of chaos and fractal models to water quality time series prediction”, Environmental Modelling &Software, No. 24, pp. 632 – 636, 2009.
[8]Kocak, K.; Bali, A.; Bektasoglu, B.; “Prediction of Monthly Flows by Using Chaotic Approach”. International congress on river basin management, 22-24 March,Antalya, Turkey, Chp 4, No. 117, 553 – 559,2007.
[9]Kocak, K.; Saylan, L.; Sen, O.; “Nonlinear Time Series Prediction of O3 Concentration In Istanbul”, Atmosphere Environment, No. 34,pp. 1267 - 1271, 2000.
[10]Kennel, M.; Brown, R.; Abarbanel, H.D.I.; “Determining embedding dimension for phasespace reconstruction using a geometrical construction”. Physical Review A, No. 45(6),pp. 3403 – 3411, 1992.
[11]Shang, P.; Na, X.; Kamae, S.; “Chaotic analysis of time series in the sediment transport phenomenon”, Chaos, Solitons and Fractals, No. 41, pp. 368 – 379, 2009.
[12] Khan, S.; Ganguly, A.R.; Saigal, S.; “Detection and Predictive Modeling of Chaos in Finite Hydrological Time Series”,Nonlinear Processes in Geophysics, No. 12,pp., 41 – 53,2005.
[13] Regonda, S.K.; Sivakumar, B.; Jain, A.; “Temporal scaling in river flow: can it be chaotic?”, Hydrological Sciences–Journal–des Sciences Hydrologiques, No. 49(3), June.2004.
[14]Solomatine, D.P.; Velickov, S.; Wust, J.C.; “Predicting Water Levels and Currents in the North Sea Using Chaos Theory and Neural Networks”. Proc. 29 th Iahr Congress, Beijing, China, September, pp. 1-11, 2001.
[15]Stehlik, J.; “Deterministic Chaos in Runoff Series”. Czech Hydro meteorological institute, Dept of Experimental Hydrology, 143, 06 prague, 2003.
[16]Takens, F.; “Detecting strange attractors in turbulence”. In: Rand, D.A., Young, L.S. (Eds.), Dynamical Systems and Turbulence, Lecture Notes in Mathematics, vol. 898, pp: 366–381 , 1981.
[17]Ng, W.W.; Panu, U.S.; Lennox, W.C., “Chaos based Analytical techniques for daily extreme hydrological observations”, Journal of Hydrology, No. 342, pp. 17 – 41, 2007.
[18]Zaldivar, J.M.; Strozzi, F.; Gutierrez, E.; Shepherd, I.M.; Tomasin, A.; “Early detection of high water at Venice Lagoon using chaos theory techniques”. In Babovic, V., Larsen, L.C. (Eds.), Proceedings of the Third International Conference on Hydro informatics ’98, vol. 2. Danish Hydraulic Institute, Copenhagen, pp. 1483 – 1490, 1998.

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