Preview

GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY

Advanced search

CASPIAN SEA LEVEL PREDICTION USING ARTIFICIAL NEURAL NETWORK AND EMPIRICAL MODE DECOMPOSITION

https://doi.org/10.24057/2071-9388-2010-3-4-25-31

Full Text:

Abstract

This paper demonstrates the possibility of using nonlinear modeling for prediction of the Caspian Sea level. Phase space geometry of the of a model can be reconstructed by the embedology methods. Dynamical invariants, such as the Lyapunov exponents, the Kaplan-Yorke dimension, and the prediction horizon were estimated from reconstruction. Fractal and multifractal analyses were carried out for various time intervals of the Caspian Sea level and multifractal spectra were calculated. Then, historical data resolution was improved with the help of fractal approximation. The EMD method was used to reduce noise of the time series. Global nonlinear predictions were made with the help of Artificial Neural Network for combinations of different empirical modes.

About the Authors

Nikolai Makarenko

Russian Federation
Leading scientist, Pulkovo Astronomical Observatory, Saint-Petersburg, Russia
Institute of Mathematics, Pushkin str. 200010, Almaty, Kazakhstan


Lyailya Karimova

Kazakhstan
Principal scientist, Institute of Mathematics, Pushkin str. 200010, Almaty, Kazakhstan



Olga Kruglun

Kazakhstan
Senior scientist, Institute of Mathematics, Pushkin str. 200010, Almaty, Kazakhstan



References

1. Barnsley, M.F. (2000) Fractals Everywhere. Hawley Rising, 550 p.

2. Bishop, Ch.M., (2006) Pattern Recognition and Machine Learning. Springer, 738 p.

3. Cochran,W.O., Hart J.C., Patrick J. F. (1998) On Approximating Rough Curves With Fractal

4. Functions. Proceedings of the Graphics Interface 1998 Conference, June 18–20, 1998,

5. Vancouver, BC, Canada. pp. 65–72.

6. Flandrin, P., Gonçalves, P. (2004) Empirical mode decompositions as data-driven wavelet-like

7. expansions. Inter. Journ. of Wavelets, Multiresolution and Information Processing. Vol. 2, pp. 1–20.

8. Golitsyn, G.S. (1995) Changes in the Caspian Sea Level as aProblem for Diagnosing and

9. Predicting Regional Climatic Changes. Izv. RAN, Fiz. Atm. Okeana Vol. 31, pp. 385–391. (in

10. Russian).

11. Halsey, T.C., Jensen, M.H., Kadanoff, L.P., Procaccia, I., Schraiman, B.I. (1968) Fractal measures and

12. their singularities: the characterizations of strange sets. Phys. Rev. A., Vol. 33, pp. 1141–1151.

13. Huang, N.E., Shen, Zh., Long, St.R., and et al. (1998) The empirical mode decomposition

14. and the Hilbert spectrum for nonlinear and non-stationary time series analysis Proc. R. Soc.

15. Lond. A., Vol. 454, pp. 903–995.

16. Karimova, L., Mukhamejanova, S., Makarenko, N. (2003) Fractal geometry methods and

17. neurocomputing for Caspian Sea level forecasting. EGS – AGU – EUG Joint Assembly,

18. Abstracts from the meeting held in Nice, France, 6–11 April 2003, abstract #186.

19. Karimova, L.M., Kuandykov, Y.B., Makarenko, N.G., Novak, M.M., Helama, S., (2007) Fractal

20. and topological dynamics for the analysis of paleoclimatic records. Physica A: Statistical

21. Mechanics and its Applications, N 373, pp. 737–746.

22. Kozhevnikova, I.A., Shveikina, V.I. (2008) Nonlinear dynamics of level in the Caspian Sea.

23. Water Resources, Vol. 35, pp. 297–304.

24. Makarenko, N.G., Karimova, L.M., Kyandykov, Y.B., Novak, M.M. (2004) Nonlinear Dynamics

25. and Prediction of the Caspian Sea Level. Thinking in Patterns, M. M. Novak (ed.). World

26. Scientific, pp. 91–102.

27. Makarenko, N.G. (2003) Embedology and Neural Networks, Moscow. Lectures on

28. neuroinformatics. “ Neuroinformatics-2003”, part. 1, pp. 86–148, (in Russian).

29. Poggio, T., Girosi, F. A (1989) Theory of networks for approximation and learning. MIT AI

30. Lab. Technical Report, Memo No. 1140, Paper No. 31.

31. Riedi, R. H., Scheuring, I., (1997) Conditional and Relative Multifractal Spectra. Fractals, N 5,

32. pp. 153–168.

33. Sauer, T., Yorke, J.A., Casdagli, M. (1991) Embedology. J. Statist. Phys., Vol. 65, pp. 579–616.

34. Schaw, R. (1981) Strange attractors, chaotic behavior, and information flow. Z. Naturforsch,

35. Vol. 36a, pp. 80–112.


For citation:


Makarenko N., Karimova L., Kruglun O. CASPIAN SEA LEVEL PREDICTION USING ARTIFICIAL NEURAL NETWORK AND EMPIRICAL MODE DECOMPOSITION. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2010;3(4):25-31. https://doi.org/10.24057/2071-9388-2010-3-4-25-31

Views: 202


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2071-9388 (Print)
ISSN 2542-1565 (Online)