An Application of Dempster-Shafer Theory to ...

Object Details

View

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

An Application of Dempster-Shafer 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: 2006-06-18     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 Dempster-Shafer 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.