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Entropy 2013, 15(6), 2398-2414; doi:10.3390/e15062398

On the Use of Information Theory to Quantify Parameter Uncertainty in Groundwater Modeling

1 Black and Veatch, 5750 Castle Creek Pkwy N Dr, Indianapolis, Indiana 46250, USA 2 Department of Geosciences, University of Missouri, Kansas City, 5100 Rockhill Road, Kansas City, Missouri, 64110, USA
* Author to whom correspondence should be addressed.
Received: 16 February 2013 / Revised: 3 June 2013 / Accepted: 5 June 2013 / Published: 13 June 2013
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences)
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We applied information theory to quantify parameter uncertainty in a groundwater flow model. A number of parameters in groundwater modeling are often used with lack of knowledge of site conditions due to heterogeneity of hydrogeologic properties and limited access to complex geologic structures. The present Information Theory-based (ITb) approach is to adopt entropy as a measure of uncertainty at the most probable state of hydrogeologic conditions. The most probable conditions are those at which the groundwater model is optimized with respect to the uncertain parameters. An analytical solution to estimate parameter uncertainty is derived by maximizing the entropy subject to constraints imposed by observation data. MODFLOW-2000 is implemented to simulate the groundwater system and to optimize the unknown parameters. The ITb approach is demonstrated with a three-dimensional synthetic model application and a case study of the Kansas City Plant. Hydraulic heads are the observations and hydraulic conductivities are assumed to be the unknown parameters. The applications show that ITb is capable of identifying which inputs of a groundwater model are the most uncertain and what statistical information can be used for site exploration.
Keywords: information theory; groundwater modeling; parameter uncertainty information theory; groundwater modeling; parameter uncertainty
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Noronha, A.; Lee, J. On the Use of Information Theory to Quantify Parameter Uncertainty in Groundwater Modeling. Entropy 2013, 15, 2398-2414.

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