Next Article in Journal
Log-Determinant Divergences Revisited: Alpha-Beta and Gamma Log-Det Divergences
Next Article in Special Issue
A Hybrid Physical and Maximum-Entropy Landslide Susceptibility Model
Previous Article in Journal
Maximum Entropy Method for Operational Loads Feedback Using Concrete Dam Displacement
Previous Article in Special Issue
Integrating Entropy and Copula Theories for Hydrologic Modeling and Analysis
Article Menu

Export Article

Open AccessArticle
Entropy 2015, 17(5), 2973-2987; doi:10.3390/e17052973

Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes

1
Department of Field Crops and Vegetables, Faculty of Agriculture, University of Novi Sad, Novi Sad 21000, Serbia
2
Department of Physics, Faculty of Sciences, University of Novi Sad, Novi Sad 21000, Serbia
3
Department of Geography, Faculty of Sciences, University of Sarajevo, Sarajevo 71000, Bosnia and Herzegovina
*
Author to whom correspondence should be addressed.
Academic Editor: Nathaniel A. Brunsell
Received: 14 January 2015 / Revised: 26 March 2015 / Accepted: 6 May 2015 / Published: 8 May 2015
(This article belongs to the Special Issue Entropy in Hydrology)
View Full-Text   |   Download PDF [4703 KB, uploaded 8 May 2015]   |  

Abstract

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. In particular, we have examined: (i) the Neretva, Bosnia and the Drina (mountain and lowland parts), (ii) the Miljacka and the Una (mountain part) and the Vrbas and the Ukrina (lowland part) and then 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. View Full-Text
Keywords: river flow time series; lower Kolmogorov complexity; upper Kolmogorov complexity; Kolmogorov complexity spectrum; Kolmogorov complexity spectrum highest value; overall Kolmogorov complexity river flow time series; lower Kolmogorov complexity; upper Kolmogorov complexity; Kolmogorov complexity spectrum; Kolmogorov complexity spectrum highest value; overall Kolmogorov complexity
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Mihailović, D.T.; Mimić, G.; Drešković, N.; Arsenić, I. Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes. Entropy 2015, 17, 2973-2987.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top