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Volume 8, Issue 8 (Suppl) J Earth Sci Clim Change
ISSN: 2157-7617 JESCC, an open access journal Earth Science Congress 2017
September 18-19, 2017
September 18-19, 2017 Hong Kong
6th International Conference on
Earth Science and Climate Change
Kolmogorov complexity based measures applied to the analysis of different river flow regimes
Dragutin T Mihailovic
University of Novi Sad, Serbia
S cientists in different fields study behavior of rivers, which is significantly influenced by human activities, climatic change
and many other factors that change mass and energy balance of the rivers. Influenced by the aforementioned factors, the
river flow may range from being simple to complex, fluctuating in both time and space. Therefore, it is of interest to determine
the nature of complexity in river flow processes, in particular in different parts of its course that cannot be done by traditional
mathematical statistics which requires the use of different measures of complexity. It seems that one of the key problems in
hydrology is that instead of use of complexity measures in analysis of river flow, hydrologists rather use traditional statistical
methods, which are not usually adequate since they are mostly based on assumptions which cannot find a niche in complex
systems analysis. We have used the Kolmogorov complexities and the Kolmogorov complexity spectrum to quantify the
randomness degree in river flow time series of seven rivers with different regimes in Bosnia and Herzegovina, representing
their different type of courses, for the period 1965-1986. We have calculated the Kolmogorov Complexity (KC) based on the
Lempel-Ziv Algorithm (LZA) (lower-KCL and upper-KCU), Kolmogorov complexity spectrum highest value (KCM) and
overall Kolmogorov complexity (KCO) values for each time series. The results indicate that the KCL, KCU, KCM and KCO
values in seven rivers show some similarities regardless of the amplitude differences in their monthly flow rates. The KCL, KCU
and KCM complexities as information measures do not see a difference between time series which have different amplitude
variations but similar random components. However, it seems that the KCO information measures better takes into account
both the amplitude and the place of the components in a time series.
Biography
Dragutin T Mihailovic is a Professor in Meteorology and Environmental Fluid Mechanics at the University of Novi Sad in Serbia. He was the Visiting Professor at
University at Albany, The State University of New York at Albany, USA, Visiting Scientist at University of Agriculture, Wageningen, Netherlands and the Norwegian
Meteorological Institute, Norway. He has more than 100 peer-reviewed scientific papers in the international journals in subjects related to land-atmosphere
processes, air pollution modeling and chemical transport models, boundary layer meteorology, physics and modeling of environmental interfaces, modeling
of complex biophysical systems, nonlinear dynamics and complexity. He has edited and wrote seven books. He was the Member of the Editorial Board of
Environmental Modeling and Software (1992-2010) and Reviewer in several scientific journals. He was the Principal Investigator in many international projects with
USA and several European countries.
Dragutin T Mihailovic, J Earth Sci Clim Change 2017, 8:8 (Suppl)
DOI: 10.4172/2157-7617-C1-030