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Entropy 2014, 16(3), 1365-1375; doi:10.3390/e16031365
Article

Ensemble Entropy for Monitoring Network Design

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Received: 21 January 2014; in revised form: 25 February 2014 / Accepted: 26 February 2014 / Published: 4 March 2014
(This article belongs to the Special Issue Entropy in Hydrology)
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Abstract: Information-theory provides, among others, conceptual methods to quantify the amount of information contained in single random variables and methods to quantify the amount of information contained and shared among two or more variables. Although these concepts have been successfully applied in hydrology and other fields, the evaluation of these quantities is sensitive to different assumptions in the estimation of probabilities. An example is the histogram bin size used to estimate probabilities to calculate Information Theory quantities via frequency methods. The present research aims at introducing a method to take into consideration the uncertainty coming from these parameters in the evaluation of the North Sea’s water level network. The main idea is that the entropy of a random variable can be represented as a probability distribution of possible values, instead of entropy being a deterministic value. The method consists of solving multiple scenarios of Multi-Objective Optimization Problem in which information content is maximized and redundancy is minimized. Results include probabilistic analysis of the chosen parameters on the resulting family of Pareto fronts, providing additional criteria on the selection of the final set of monitoring points.
Keywords: entropy; monitoring networks; uncertainty; multi-objective optimization; North Sea entropy; monitoring networks; uncertainty; multi-objective optimization; North Sea
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.

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MDPI and ACS Style

Alfonso, L.; Ridolfi, E.; Gaytan-Aguilar, S.; Napolitano, F.; Russo, F. Ensemble Entropy for Monitoring Network Design. Entropy 2014, 16, 1365-1375.

AMA Style

Alfonso L, Ridolfi E, Gaytan-Aguilar S, Napolitano F, Russo F. Ensemble Entropy for Monitoring Network Design. Entropy. 2014; 16(3):1365-1375.

Chicago/Turabian Style

Alfonso, Leonardo; Ridolfi, Elena; Gaytan-Aguilar, Sandra; Napolitano, Francesco; Russo, Fabio. 2014. "Ensemble Entropy for Monitoring Network Design." Entropy 16, no. 3: 1365-1375.


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