Modelling and forecasting the hydroecological systems pollution dynamics by using a chaos theory methods: II. advanced chaos data on pollution of the Small Carpathians river’s watersheds
Abstract
This paper goes on our advanced quantitative studying results of a pollution dynamics for variations hydroecological systems, namely, the nitrates etc concentrations dynamics for a number of the Small Carpathians river’s watersheds in the Eastern Slovakia. The different methods and algorithms of the chaos theory (chaos-geometric approach) and dynamical systems theory have been used in the advanced versions. New more exact data on chaotic behaviour of the nitrates concentration time series in the watersheds of the Small Carpathians are presented. In previous paper [1] to reconstruct the corresponding attractor, the time delay and embedding dimension have been needed and computed. The parameters are determined by the methods of autocorrelation function and average mutual information. Besides, there are used the advanced versions of the correlation dimension method and algorithm of false nearest neighbours. The Fourier spectrum of the con-centration of nitrates in the water catchment area Ondava: Stropkov for the period 1969-1996 is listed. Here we present new advanced data on the correlation dimension (d2), embedding dimension (dE), Kaplan-Yorke dimension (dL), average limit of predictability (Prmax) and parameter K for the nitrates concentrations in the watersheds of the Small Carpathians.
References
Glushkov A.V., Khetselius O.Yu., Bunyakova Yu.Ya., Prepelitsa G.P., Solyanikova E.P., Serga E.N. Non-linear prediction method in short-range forecast of atmospheric pollutants: low-dimensional chaos. Dynamical Systems - Theory and Applications. Łódź, Poland, 2011, no. LIF111, pp. 1-6.
Glushkov A.V., Kuzakon’ V.M., Khetselius O.Yu., Prepelitsa G.P., Svinarenko A.A., Zaichko P.A. Geometry of Chaos: Theoretical basis’s of a consistent combined approach to treating chaotic dynamical systems and their parameters determination. Proceedings of International Geometry Center, 2013, no. 6 (1), pp. 43-48.
Glushkov A.V., Kuzakon’ V.M., Khetselius O.Yu., Bunyakova Yu.Ya., Zaichko P.A., Geometry of Chaos: Consistent combined approach to treating chaotic dynamics atmospheric polutants and its forecasting. Proceedings of International Geometry Center, 2013, no. 6 (3), pp. 6-13.
Glushkov A.V., Serbov N.G., Svinarenko A.A., Buyadzhi V.V. Chaos-geometric analysis of time series of concentrations of sulphur dioxide in the atmosphere of the industrial city (on example of the Gdansk region. Ukr. gìdrometeorol. ž.– Ukranian hydrometeorological journal, 2014, no.15, pp.238-241.
Glushkov A.V., Khokhlov V.N., Tsenenko I.A. Atmospheric teleconnection patterns: wavelet analysis. Nonlinear Processes in Geophysics, 2004, no. 11(3), pp. 285-293.
Khokhlov V.N., Glushkov A.V., Loboda N.S, Bunyakova Yu.Ya. Short-range forecast of atmospheric pollutants using non-linear prediction method. Atmospheric Environment (Elsevier), 2008, no. 42, pp. 1213-1220.
Khokhlov V.N., Glushkov A.V., Loboda N.S., Serbov N.G., Zhurbenko K. Signatures of low-dimensional chaos in hourly water level measurements at coastal site of Mariupol, Ukraine. Stoch Environ Res Risk Assess (Springer), 2008, no. 22 (6), pp. 777-788.
Bunyakova Yu.Ya., Glushkov A.V. Analysis and forecast of the impact of anthropogenic factors on air basein of an industrial city. Odessa: Ecology, 2010. 256 p.
Glushkov A.V., Khokhlov V.N., Serbov N.G., Bunyakova Yu.Ya., Balan A.K., Balanyuk E.P. Low-dimensional chaos in the time series of concentrations of pollutants in an atmosphere and hydrosphere. Vìsn. Odes. derž. ekol. unìv.– Bulletin of Odessa state environmental university , 2007, no.4, pp.337-348.
Glushkov A.V. Analysis and forecast of the anthropogenic impact on industrial city’s atmosphere based on methods of chaos theory: new general scheme. Ukr. gìdrometeorol. ž.– Ukranian hydrometeorological journal, 2014, no.15, pp.32-36.
Serbov N.G., Khetselius O.Yu., Balan A.K., Dudinov A.A. Prediction of chaotic processes in hydro systems based on the concept of attractor neural network and campaign. Ukr. gìdrometeorol. ž. - Ukranian hydrometeorological journal, 2013, no.13, pp.86-91.
Serbov N.G., Balan A.K., Solyanikova E.P. Multivariate system and multifractal approaches in modeling extreme high floods (for example, Danube river) and temporal fluctuations in the concentrations of pollutants in the river water. Vìsn. Odes. derž. ekol. unìv.– Bulletin of Odessa state environmental university, 2008, no.6, pp.7-13.
Kennel M.B., Brown R., Abarbanel H. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A., 1992, no. 45, pp. 3403-3411.
Pekarova P., Miklanek P., Konicek A., Pekar J. Water quality in experimental basins. National Report 1999 of the UNESKO.-Project 1.1.-Intern.Water Systems, 1999, pp. 1-98.
Glushkov A.V. Methods of a chaos theory. Odessa: Astroprint, 2012.
Gallager R.G. Information theory and reliable communication. New-York, Wiley, 1986.
Lanfredi M., Macchiato M. Searching for low dimensionality in air pollution time series. Europhysics Letters 1997, 1997, pp. 589-594.
Koçak K., Şaylan L., Şen O. Nonlinear time series prediction of O3 concentration in Istanbul. Atmospheric Environment (Elsevier), 2000, no. 34, pp. 1267-1271.
Grassberger P., Procaccia I. Measuring the strangeness of strange attractors. Physica D., 1983, no. 9, pp. 189-208.
Gottwald G.A., Melbourne I. A new test for chaos in deterministic systems. Proc. Roy. Soc. London. Ser. A, Mathemat. Phys. Sci., 2004, no. 460, pp. 603-611.
Svoboda A., Pekarova P., Miklanek P. Flood hydrology of Danube between Devin and Nagymaros in Slovakia. Nat. Rep.2000, UNE-SKO.-Project 4.1.-Intern.Water Systems. 2000. 96 p.
Pekarova P., Miklanek P., Konicek A., Pekar J. Water quality in experimental basins. Nat. Rep.1999 of the UNESKO.-Project 1.1.-Intern.Water Systems. 1999. 98 p.
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