Application of linear genetic programming in conjunction with Kohonen's self-organizing map and ensemble empirical mode decomposition for improved streamflow prediction at lock and dam 10 along the Kentucky River

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dc.contributor.advisorSharif, Hatim
dc.contributor.authorBarge, Jonathan T.
dc.contributor.committeeMemberWeissling, Blake
dc.contributor.committeeMemberXie, Hongjie
dc.date.accessioned2024-01-25T22:33:45Z
dc.date.available2024-01-25T22:33:45Z
dc.date.issued2015
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractThe accurate prediction of streamflow from precipitation and runoff information has been widely studied due to the complexities of the rainfall-runoff process. Popularized data-driven techniques like artificial neural networks (ANNs) and genetic programming (GP) have proven to be useful alternatives to more complicated conceptual and physically based models. Linear genetic programming (LGP), which is applied in this study, differs from GP in that it allows faster processing times and a greater abundance of solutions. The application of LGP on hydrologic information from the Kentucky River Basin resulted in slightly improved forecasting when compared to ANN models. To further explore the capability of LGP, hybrid models incorporating the data decomposition technique of ensemble empirical mode decomposition (EEMD) and the data clustering technique of a self-organizing map (SOM) were applied to the same study area. The EEMD-SOM-LGP hybrid model proved to significantly outperform the utilization of LGP on its own.
dc.description.departmentGeosciences
dc.format.extent102 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9781321734348
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2461
dc.languageen
dc.subjectEnvironmental Science
dc.subjectHydrologic Modeling
dc.subjectHydrology
dc.subject.classificationHydrologic sciences
dc.subject.classificationEnvironmental science
dc.subject.classificationEnvironmental engineering
dc.subject.lcshStreamflow -- Kentucky -- Kentucky River -- Mathematical models
dc.subject.lcshStream measurements -- Kentucky -- Kentucky River -- Linear programming
dc.subject.lcshGenetic programming (Computer science)
dc.titleApplication of linear genetic programming in conjunction with Kohonen's self-organizing map and ensemble empirical mode decomposition for improved streamflow prediction at lock and dam 10 along the Kentucky River
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentGeosciences
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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