Optimizing Groundwater Remediation Designs Using Dynamic Meta-Models and Genetic Algorithms
Yan, Shengquan
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https://hdl.handle.net/2142/83308
Description
Title
Optimizing Groundwater Remediation Designs Using Dynamic Meta-Models and Genetic Algorithms
Author(s)
Yan, Shengquan
Issue Date
2006
Doctoral Committee Chair(s)
Barbara Minsker
Department of Study
Civl and Environmental Engineering
Discipline
Civl and Environmental Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Environmental Sciences
Language
eng
Abstract
Real-world optimization problems are often inherently uncertain. The last focus of the research is to extend the adaptive modeling technique in a stochastic optimization framework so that robust optimal solutions can be efficiently identified in the presence of parameter uncertainty. The developed algorithm, called Noisy-AMGA, minimizes the expected fitness function with a constrained reliability level. As in AMGA, the meta-models in Noisy-AMGA are online updated but they are trained to predict the expected outputs. The method was applied to two remediation case studies, where the primary source of uncertainty stems from hydraulic conductivity values in the aquifers. The results show that the technique can lead to far more reliable solutions with significantly less computational effort.
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