Objective functions for information-theoretical monitoring network design: what is “optimal”?

<p>This paper concerns the problem of optimal monitoring network layout using information-theoretical methods. Numerous different objectives based on information measures have been proposed in recent literature, often focusing simultaneously on maximum information and minimum dependence betwee...

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Main Authors: H. Foroozand, S. V. Weijs
Format: Article
Language:English
Published: Copernicus Publications 2021-02-01
Series:Hydrology and Earth System Sciences
Online Access:https://hess.copernicus.org/articles/25/831/2021/hess-25-831-2021.pdf
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spelling doaj-892b7e490e9b45c2977a22d4bc986d482021-02-19T11:42:08ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382021-02-012583185010.5194/hess-25-831-2021Objective functions for information-theoretical monitoring network design: what is “optimal”?H. ForoozandS. V. Weijs<p>This paper concerns the problem of optimal monitoring network layout using information-theoretical methods. Numerous different objectives based on information measures have been proposed in recent literature, often focusing simultaneously on maximum information and minimum dependence between the chosen locations for data collection stations. We discuss these objective functions and conclude that a single-objective optimization of joint entropy suffices to maximize the collection of information for a given number of stations. We argue that the widespread notion of minimizing redundancy, or dependence between monitored signals, as a secondary objective is not desirable and has no intrinsic justification. The negative effect of redundancy on total collected information is already accounted for in joint entropy, which measures total information net of any redundancies. In fact, for two networks of equal joint entropy, the one with a higher amount of redundant information should be preferred for reasons of robustness against failure. In attaining the maximum joint entropy objective, we investigate exhaustive optimization, a more computationally tractable greedy approach that adds one station at a time, and we introduce the “greedy drop” approach, where the full set of stations is reduced one at a time. We show that no greedy approach can exist that is guaranteed to reach the global optimum.</p>https://hess.copernicus.org/articles/25/831/2021/hess-25-831-2021.pdf
collection DOAJ
language English
format Article
sources DOAJ
author H. Foroozand
S. V. Weijs
spellingShingle H. Foroozand
S. V. Weijs
Objective functions for information-theoretical monitoring network design: what is “optimal”?
Hydrology and Earth System Sciences
author_facet H. Foroozand
S. V. Weijs
author_sort H. Foroozand
title Objective functions for information-theoretical monitoring network design: what is “optimal”?
title_short Objective functions for information-theoretical monitoring network design: what is “optimal”?
title_full Objective functions for information-theoretical monitoring network design: what is “optimal”?
title_fullStr Objective functions for information-theoretical monitoring network design: what is “optimal”?
title_full_unstemmed Objective functions for information-theoretical monitoring network design: what is “optimal”?
title_sort objective functions for information-theoretical monitoring network design: what is “optimal”?
publisher Copernicus Publications
series Hydrology and Earth System Sciences
issn 1027-5606
1607-7938
publishDate 2021-02-01
description <p>This paper concerns the problem of optimal monitoring network layout using information-theoretical methods. Numerous different objectives based on information measures have been proposed in recent literature, often focusing simultaneously on maximum information and minimum dependence between the chosen locations for data collection stations. We discuss these objective functions and conclude that a single-objective optimization of joint entropy suffices to maximize the collection of information for a given number of stations. We argue that the widespread notion of minimizing redundancy, or dependence between monitored signals, as a secondary objective is not desirable and has no intrinsic justification. The negative effect of redundancy on total collected information is already accounted for in joint entropy, which measures total information net of any redundancies. In fact, for two networks of equal joint entropy, the one with a higher amount of redundant information should be preferred for reasons of robustness against failure. In attaining the maximum joint entropy objective, we investigate exhaustive optimization, a more computationally tractable greedy approach that adds one station at a time, and we introduce the “greedy drop” approach, where the full set of stations is reduced one at a time. We show that no greedy approach can exist that is guaranteed to reach the global optimum.</p>
url https://hess.copernicus.org/articles/25/831/2021/hess-25-831-2021.pdf
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