HR: 1340h
AN: H13B-0412    [Abstracts]
TI: Integration of Fuzzy and Probabilistic Information in the Description of Hydraulic Conductivity
AU: * Druschel, B
EM: bree.druschel@uvm.edu
AF: Research Center for Groundwater Remediation Design, University of Vermont, 213 Votey Building, Burlington, VT 05405 United States
AU: Ozbek, M
EM: ozbek@emba.uvm.edu
AF: Research Center for Groundwater Remediation Design, University of Vermont, 213 Votey Building, Burlington, VT 05405 United States
AU: Pinder, G
EM: pinder@emba.uvm.edu
AF: Research Center for Groundwater Remediation Design, University of Vermont, 213 Votey Building, Burlington, VT 05405 United States
AB: Evaluation of the heterogeneity of hydraulic conductivity, K, is a well-known problem in groundwater hydrology. The open question is how to fully represent a given highly heterogeneous K field and its inherent uncertainty at least cost. Today, most K fields are analyzed using field test data and probability theory. Uncertainty is usually reported in the spatial covariance. In an attempt to develop a more cost effective method which still provides an accurate approximation of a K field, we propose using an evidence theory framework to merge probabilistic and fuzzy (or possibilistic) information in an effort to improve our ability to fully define a K field. The tool chosen to fuse probabilistic information obtained via experiment and subjective information provided by the groundwater professional is Dempster's Rule of Combination. In using this theory we must create mass assignments for our subject of interest, describing the degree of evidence that supports the presence of our subject in a particular set. These mass assignments can be created directly from the probabilistic information and, in the case of the subjective information, from feedback we obtain from an expert. The fusion of these two types of information provides a better description of uncertainty than would typically be available with just probability theory alone.
DE: 1829 Groundwater hydrology
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting