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Discrete Dynamics in Nature and Society
Volume 1, Issue 4, Pages 255-264

Predictability problems of global change as seen through natural systems complexity description. 2. Approach

1Institute of Computational Mathematics, Russian Academy of Sciences, Gubkine Street 8, Moscow 117333, Russia
2M. V. Lomonosov Moscow State University, Vorobiovy Gory, MSU, Moscow 119899, Russia

Received 2 June 1997

Copyright © 1998 Hindawi Publishing Corporation. 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.


Developing the general statements of the proposed global change theory, outlined in Part 1 of the publication, Kolmogorov's probability space is used to study properties of information measures (unconditional, joint and conditional entropies, information divergence, mutual information, etc.). Sets of elementary events, the specified algebra of their sub-sets and probability measures for the algebra are composite parts of the space. The information measures are analyzed using the mathematical expectance operator and the adequacy between an additive function of sets and their equivalents in the form of the measures. As a result, explanations are given to multispectral satellite imagery visualization procedures using Markov's chains of random variables represented by pixels of the imagery. The proposed formalism of the information measures application enables to describe the natural targets complexity by syntactically governing probabilities. Asserted as that of signal/noise ratios finding for anomalies of natural processes, the predictability problem is solved by analyses of temporal data sets of related measurements for key regions and their background within contextually coherent structures of natural targets and between particular boundaries of the structures.