Data-Driven Models to Enhance Physically-Based Groundwater Model Predictions
Demissie, Yonas Kassa
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https://hdl.handle.net/2142/83370
Description
Title
Data-Driven Models to Enhance Physically-Based Groundwater Model Predictions
Author(s)
Demissie, Yonas Kassa
Issue Date
2008
Doctoral Committee Chair(s)
Valocchi, Albert J.
Department of Study
Civil Engineering
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
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.
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