A neural network-based method for evaluating a spatially distributed parameter field: An application in groundwater remediation under uncertainty

Uncertainty due to spatial variability of hydraulic conductivity is an important issue in the design of reliable groundwater remediation strategies. Using groundwater management models based on a stochastic approach to groundwater flow, where the log-hydraulic conductivity is represented as a random field, is a frequently studied technique for the design of aquifer remediation in the presence of uncertainty. Such an approach employs the solution of a management model for a large set of equally probable realizations of the hydraulic conductivity. However, only a few critical realizations out of the large set will influence the final design. Incorporation of only a few of the critical realizations in the design procedure would result in a robust design with high reliability level. This reliability level is comparable to those of the designs obtained using many realizations.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Ranjithan, S.

Contributors dc:contributor

• Eheart, J. Wayland

• Engineering, Civil
• Engineering, Sanitary and Municipal
• Environmental Sciences
• Artificial Intelligence

Statement dc:rights

• Copyright 1992 Ranjithan, S.

Language dc:language

eng

Identifier

AAI9305662
(UMI)AAI9305662

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/20393