Probabilistic Graphical Modeling Method for Inferring Hydraulic Conductivity Maps from Hydraulic Head Maps
Publication: Journal of Hydrologic Engineering
Volume 21, Issue 2
Abstract
The ability to design and employ groundwater distribution models plays an important role in the development and application of regional water management policies and resource exploration. This paper presents a probabilistic reasoning approach for estimating groundwater levels over a geological map based on a limited number of available observations of hydraulic head and conductivity levels. The approach adapts, expands, and combines non-Euclidean distance kriging, probabilistic graphical modeling, and expectation maximization to provide a viable alternative to the currently existing, simulation-based methods of spatial interpolation. Upon outlining a conceptual framework for the proposed approach, this paper investigates the feasibility of using its key component, the Markov random field, with a flexible (learned) structure that recovers hydraulic conductivity maps from the knowledge of hydraulic head on those maps. The model is trained on a medium-sized data set of simulated hydraulic maps, and returns promising results. The paper also motivates future work in the area, pointing out several research directions.
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Information & Authors
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Published In
Journal of Hydrologic Engineering
Volume 21 • Issue 2 • February 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Oct 24, 2014
Accepted: Jun 17, 2015
Published online: Jul 23, 2015
Discussion open until: Dec 23, 2015
Published in print: Feb 1, 2016
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