{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/83370"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/83370","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Data-Driven Models to Enhance Physically-Based Groundwater Model Predictions","abstract":"Finally, the applicability of the methodologies and the validity of the complementary modeling framework are tested using both hypothetical and real-world groundwater flow problems of varying complexity. The results indicate that the complementary modeling framework presents a promising and viable alternative to improve groundwater flow predictions, especially, those related to long-term temporal predictions at observation wells and spatial predictions at arbitrary locations. For the real-world groundwater flow problem, the complementary modeling framework reduced MODFLOW's root-mean-square errors (RMSE) for temporal and spatial head predictions by about 78% and 67%, respectively. The uncertainty analysis techniques also significantly improve the estimated 95% confidence and predictions intervals. The percentage of data coverage by the intervals is improved by as much as 88%, while the width of the intervals is diminished.","abstract_html":"Finally, the applicability of the methodologies and the validity of the complementary modeling framework are tested using both hypothetical and real-world groundwater flow problems of varying complexity. The results indicate that the complementary modeling framework presents a promising and viable alternative to improve groundwater flow predictions, especially, those related to long-term temporal predictions at observation wells and spatial predictions at arbitrary locations. For the real-world groundwater flow problem, the complementary modeling framework reduced MODFLOW's root-mean-square errors (RMSE) for temporal and spatial head predictions by about 78% and 67%, respectively. The uncertainty analysis techniques also significantly improve the estimated 95% confidence and predictions intervals. The percentage of data coverage by the intervals is improved by as much as 88%, while the width of the intervals is diminished.","abstract_has_math":false,"creators":["Demissie, Yonas Kassa"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Valocchi, Albert J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T21:04:33Z","date_published":"2015-09-25T21:04:33Z","updated_at":"2026-07-22T22:26:21Z","subjects":["Computer Science"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3314759"],"render_values":[{"text":"(MiAaPQ)AAI3314759","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/83370","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Valocchi, Albert J."]},{"key":"dc:creator","label":"Author","values":["Demissie, Yonas Kassa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T21:04:33Z","10000-01-01","2008"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil 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":["Computer Science"]}]},{"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/2142/83370","(MiAaPQ)AAI3314759"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Finally, the applicability of the methodologies and the validity of the complementary modeling framework are tested using both hypothetical and real-world groundwater flow problems of varying complexity. The results indicate that the complementary modeling framework presents a promising and viable alternative to improve groundwater flow predictions, especially, those related to long-term temporal predictions at observation wells and spatial predictions at arbitrary locations. For the real-world groundwater flow problem, the complementary modeling framework reduced MODFLOW's root-mean-square errors (RMSE) for temporal and spatial head predictions by about 78% and 67%, respectively. The uncertainty analysis techniques also significantly improve the estimated 95% confidence and predictions intervals. The percentage of data coverage by the intervals is improved by as much as 88%, while the width of the intervals is diminished.","Made available in DSpace on 2015-09-25T21:04:33Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3314759.pdf: 3482431 bytes, checksum: d44724ad852e0c29b54aeefbf2c3ba84 (MD5) Previous issue date: 2008","Embargo set by: Seth Robbins for item 84651 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","219 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008."]},{"key":"dc:title","label":"Title","values":["Data-Driven Models to Enhance Physically-Based Groundwater Model Predictions"]}]}],"canonical_facts":{"dc:contributor":["Valocchi, Albert J."],"dc:creator":["Demissie, Yonas Kassa"],"dc:date":["2015-09-25T21:04:33Z","10000-01-01","2008"],"dc:description":["Finally, the applicability of the methodologies and the validity of the complementary modeling framework are tested using both hypothetical and real-world groundwater flow problems of varying complexity. The results indicate that the complementary modeling framework presents a promising and viable alternative to improve groundwater flow predictions, especially, those related to long-term temporal predictions at observation wells and spatial predictions at arbitrary locations. For the real-world groundwater flow problem, the complementary modeling framework reduced MODFLOW's root-mean-square errors (RMSE) for temporal and spatial head predictions by about 78% and 67%, respectively. The uncertainty analysis techniques also significantly improve the estimated 95% confidence and predictions intervals. The percentage of data coverage by the intervals is improved by as much as 88%, while the width of the intervals is diminished.","Made available in DSpace on 2015-09-25T21:04:33Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3314759.pdf: 3482431 bytes, checksum: d44724ad852e0c29b54aeefbf2c3ba84 (MD5) Previous issue date: 2008","Embargo set by: Seth Robbins for item 84651 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","219 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008."],"dc:identifier":["http://hdl.handle.net/2142/83370","(MiAaPQ)AAI3314759"],"dc:language":["eng"],"dc:subject":["Computer Science"],"dc:title":["Data-Driven Models to Enhance Physically-Based Groundwater Model Predictions"],"dc:type":["text"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:21Z"}