{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/83308"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/83308","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optimizing Groundwater Remediation Designs Using Dynamic Meta-Models and Genetic Algorithms","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Yan, Shengquan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civl and Environmental Engineering","degree_department":null,"school":null,"contributors":["Barbara Minsker"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T21:04:12Z","date_published":"2015-09-25T21:04:12Z","updated_at":"2026-07-22T22:26:21Z","subjects":["Environmental Sciences"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3243030"],"render_values":[{"text":"(MiAaPQ)AAI3243030","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/83308","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Barbara Minsker"]},{"key":"dc:creator","label":"Author","values":["Yan, Shengquan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T21:04:12Z","10000-01-01","2006"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civl and Environmental Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Environmental Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/83308","(MiAaPQ)AAI3243030"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Real-world optimization problems are often inherently uncertain. 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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.","Made available in DSpace on 2015-09-25T21:04:12Z (GMT). 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