Optimizing Groundwater Remediation Designs Using Dynamic Meta-Models and Genetic Algorithms

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civl and Environmental Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Yan, Shengquan

Contributors dc:contributor

• Barbara Minsker

• Environmental Sciences

Language dc:language

eng

Identifier

(MiAaPQ)AAI3243030

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/83308