XVI International Conference on Computational Methods in Water Resources (CMWRXVI) Ingeniørhuset  

An Application of DempsterShafer Theory to Hydraulic Conductivity  Paper   

Author:  Bree Druschel <bree.druschel@uvm.edu> (The University of Vermont) 
 Metin Ozbek <ozbek@cems.uvm.edu> (The University of Vermont) 
 George Pinder <pinder@cems.uvm.edu> (The University of Vermont) 
Presenter:  Bree Druschel <bree.druschel@uvm.edu> (The University of Vermont) 
Date:
 20060618
Track:
General Sessions
Session:
General 
DOI:  10.4122/1.1000000323 
DOI:  10.4122/1.1000000324 

While uncertainty is an integral part of the mathematical representation of the
environment, behavior forecasting requires the use of mathematical models that
require the specification of physically based parameters descriptive of the
environment. In subsurface hydrology, for example, the hydraulic conductivity (a
measure of soil permeability) must be specified in equations descriptive of
groundwater flow.
Traditionally probability is used to characterize uncertainty in hydraulic
conductivity (K). It seeks to describe uncertainty arising from a lack of knowledge
regarding concepts that are inherently crisp and well defined. However, classical
probability itself is not applicable to situations where the concepts themselves are
vague. In this situation one must consider other avenues for assessing the
uncertainty.
It is our intention to use a DempsterShafer Theory framework to merge probabilistic
and fuzzy (subjective) information in an effort to improve our ability to fully
define a hydraulic conductivity field. The advantages over using probability theory
alone include 1) being able to use all available data to analyze hydraulic
conductivity uncertainty (outliers are kept in the analysis) and 2) not having to
make assumptions about distribution functions (e.g. typically a lognormal
distribution is used to describe hydraulic conductivity of a site). Successful
combination of subjective and empirical information will improve our ability to
properly describe subsurface heterogeneity and would result in improved models of
subsurface environments. 