Application of Deep Learning to Estimate Mean River Cross-Sectional Depth

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dc.contributorKeellings, David
dc.contributorMoftakhari, Hamed
dc.contributor.advisorCohen, Sagy
dc.contributor.authorRaney, Arthur Austin
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2021-11-23T14:33:50Z
dc.date.available2021-11-23T14:33:50Z
dc.date.issued2021
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractEstimates of riverine channel geometry play a vital role in the physical representation of stream networks in models used to predict flood and drought conditions, manage water resources, and increase our knowledge of fluvial conditions under a changing climate. A well established body of literature exists that explains the relationship between channel geometry parameters width, depth, and velocity to instantaneous river discharge using a log-log linear power-law regression. In this study, a state-of-the-art deep learning regression model is presented and compared against the power-law method to evaluate their abilities to estimate cross-sectional mean river depth. Results reveal three key findings, the neural network: (1) decreases RMSE by 22% verse a CONUS scale power-law equation, (2) reduces prediction variance across Strahler stream orders, and (3) generally outperforms regional power-law equations with an average decrease in RMSE of 8.7%. Lastly, a reach-level CONUS dataset of estimated mean river depth is delivered.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otherhttp://purl.lib.ua.edu/181453
dc.identifier.otheru0015_0000001_0003892
dc.identifier.otherRaney_alatus_0004M_14616
dc.identifier.urihttp://ir.ua.edu/handle/123456789/8124
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectDeep learning
dc.subjectHydraulic Geometry
dc.subjectHydrology
dc.subjectNeural Network
dc.titleApplication of Deep Learning to Estimate Mean River Cross-Sectional Depthen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Geography
etdms.degree.disciplineGeography
etdms.degree.grantorThe University of Alabama
etdms.degree.levelmaster's
etdms.degree.nameM.S.

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