HR: 10:35h
AN: H11I-02    [PDF]
TI: Uncertainty-Based Multi-Objective Optimization of Groundwater Remediation Design
AU: * Singh, A
EM: asingh8@uiuc.edu
AF: Department of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, 4129, NCEL-MC 250 205 N. Mathews Avenue, Urbana, IL 61801 United States
AU: Minsker, B
EM: minsker@uiuc.edu
AF: Department of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, 3230, NCEL-MC 250 205 N. Mathews Avenue, Urbana, IL 61801 United States
AB: Management of groundwater contamination is a cost-intensive undertaking filled with conflicting objectives and substantial uncertainty. A critical source of this uncertainty in groundwater remediation design problems comes from the hydraulic conductivity values for the aquifer, upon which the prediction of flow and transport of contaminants are dependent. For a remediation solution to be reliable in practice it is important that it is robust over the potential error in the model predictions. This work focuses on incorporating such uncertainty within a multi-objective optimization framework, to get reliable as well as Pareto optimal solutions. Previous research has shown that small amounts of sampling within a single-objective genetic algorithm can produce highly reliable solutions. However with multiple objectives the noise can interfere with the basic operations of a multi-objective solver, such as determining non-domination of individuals, diversity preservation, and elitism. This work proposes several approaches to improve the performance of noisy multi-objective solvers. These include a simple averaging approach, taking samples across the population (which we call extended averaging), and a stochastic optimization approach. All the approaches are tested on standard multi-objective benchmark problems and a hypothetical groundwater remediation case-study; the best-performing approach is then tested on a field-scale case at Umatilla Army Depot.
DE: 1829 Groundwater hydrology
DE: 1831 Groundwater quality
SC: Hydrology [H]
MN: 2003 Fall Meeting