Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes
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 ha...
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MDPI AG
2015-05-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/17/5/2973 |
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| author | Dragutin T. Mihailović Gordan Mimić Nusret Drešković Ilija Arsenić |
| author_facet | Dragutin T. Mihailović Gordan Mimić Nusret Drešković Ilija Arsenić |
| author_sort | Dragutin T. Mihailović |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T10:58:44Z |
| format | Article |
| id | doaj.art-70028072b8ca48388a278b555b0cea2d |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T10:58:44Z |
| publishDate | 2015-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-70028072b8ca48388a278b555b0cea2d2022-12-22T04:28:41ZengMDPI AGEntropy1099-43002015-05-011752973298710.3390/e17052973e17052973Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow RegimesDragutin T. Mihailović0Gordan Mimić1Nusret Drešković2Ilija Arsenić3Department of Field Crops and Vegetables, Faculty of Agriculture, University of Novi Sad, Novi Sad 21000, SerbiaDepartment of Physics, Faculty of Sciences, University of Novi Sad, Novi Sad 21000, SerbiaDepartment of Geography, Faculty of Sciences, University of Sarajevo, Sarajevo 71000, Bosnia and HerzegovinaDepartment of Field Crops and Vegetables, Faculty of Agriculture, University of Novi Sad, Novi Sad 21000, SerbiaWe 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.http://www.mdpi.com/1099-4300/17/5/2973river flow time serieslower Kolmogorov complexityupper Kolmogorov complexityKolmogorov complexity spectrumKolmogorov complexity spectrum highest valueoverall Kolmogorov complexity |
| spellingShingle | Dragutin T. Mihailović Gordan Mimić Nusret Drešković Ilija Arsenić Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes Entropy river flow time series lower Kolmogorov complexity upper Kolmogorov complexity Kolmogorov complexity spectrum Kolmogorov complexity spectrum highest value overall Kolmogorov complexity |
| title | Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes |
| title_full | Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes |
| title_fullStr | Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes |
| title_full_unstemmed | Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes |
| title_short | Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes |
| title_sort | kolmogorov complexity based information measures applied to the analysis of different river flow regimes |
| topic | river flow time series lower Kolmogorov complexity upper Kolmogorov complexity Kolmogorov complexity spectrum Kolmogorov complexity spectrum highest value overall Kolmogorov complexity |
| url | http://www.mdpi.com/1099-4300/17/5/2973 |
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