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Title:Data-Driven Models to Enhance Physically-Based Groundwater Model Predictions
Author(s):Demissie, Yonas Kassa
Doctoral Committee Chair(s):Valocchi, Albert J.
Department / Program:Civil Engineering
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer Science
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.
Issue Date:2008
Description:219 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.
Other Identifier(s):(MiAaPQ)AAI3314759
Date Available in IDEALS:2015-09-25
Date Deposited:2008

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