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XVIII International Conference on Water Resources
CMWR 2010
J. Carrera (Ed)
CIMNE, Barcelona 2010
GROUNDWATER FLOW AND TRANSPORT MODELING WITH
CORRELATED POSSIBILISTIC DATA
Metin M. Ozbek*, James L. Ross† and George F. Pinder+
*
ENVIRON International Corporation
214 Carnegie Center, Princeton, NJ 08540, USA
e-mail: [email protected]
†
GeoTrans Inc.
1080 Holcomb Bridge Road, Building 100, Suite 190, Roswell, GA 30076, USA
e-mail: [email protected]
+ University of Vermont
College of Engineering and Mathematical Sciences, 371 Votey Building
e-mail: [email protected]
Stochastic groundwater modeling involves the propagation of probabilistic uncertainty from
model input parameters to model estimates, usually via a Monte Carlo method. With the
increasing reliance upon expert knowledge to define model inputs, and both fuzzy set and
possibility theories to characterize this expert knowledge, alternative means of executing
model equations are needed. While the fuzzy extension principle is commonly used to
propagate such uncertainty, unless approximated, its implementation can be intensive for
transient groundwater problems; and an alternative more practical approach to modeling with
fuzzy and possibilistic data is warranted. In the proposed alternative approach, correlated
hydraulic conductivity possibility distributions are sampled using their random set
representations and crisp realizations of the hydraulic conductivity field are generated using a
Latin hypercube lattice sampling technique, and ultimately operated upon by groundwater
flow and transport model equations. The resulting uncertain concentration estimates based on
these realizations are assembled as possibility distributions. We demonstrate an application of
the approach to a site in Woburn, MA.