PREDICTIONS OF SIGNIFICANT WAVE HEIGHT IN LAKE OKEECHOBEE, FLORIDA USING APPROACHES RELATED TO SIMPLIFIED STOCHASTIC PROCEDURE AND WAVE ENERGY SPECTRUM

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dc.contributor.advisorWang, Keh-Han
dc.contributor.committeeMemberRogers, Jerry R.
dc.contributor.committeeMemberStrom, Kyle B.
dc.contributor.committeeMemberAltunkaynak, Abdüsselam
dc.creatorKhan, Ismat 1984-
dc.date.accessioned2014-12-19T13:19:22Z
dc.date.available2014-12-19T13:19:22Z
dc.date.createdDecember 2012
dc.date.issued2012-12
dc.date.updated2014-12-19T13:19:22Z
dc.description.abstractPrediction of significant wave height is critically important to the physical and environmental impact study of coastal, estuarine or large lake environments. In this study, development of predictive models for the determination of time varying significant wave heights in Lake Okeechobee, Florida using the simplified stochastic procedure and wave energy spectrum method is presented. The stochastic procedure related models are Regression Model 1 (RM1), Regression Model 2 (RM2) and Perceptron Least Square Method (PLSM). A new wave spectrum based model, Modified Pierson-Moskowitz (MPM) Spectrum is also developed. The predicted significant wave heights from each model are compared with the Artificial Neural Network (ANN) predictions obtained by Altunkaynak and Wang (2012). The comparisons between predicted significant wave heights from each model and observed data indicate that the proposed RM1, RM2, PLSM and MPM are effective models that are acceptable for predicting significant wave height in Lake Okeechobee.
dc.description.departmentCivil and Environmental Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/832
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectSignificant wave height
dc.subjectRegression Method
dc.subjectPerceptron Least Square Method
dc.subjectPierson-Moskowitz Spectrum
dc.subjectArtificial neural networks
dc.subject.lcshCivil engineering
dc.titlePREDICTIONS OF SIGNIFICANT WAVE HEIGHT IN LAKE OKEECHOBEE, FLORIDA USING APPROACHES RELATED TO SIMPLIFIED STOCHASTIC PROCEDURE AND WAVE ENERGY SPECTRUM
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentCivil and Environmental Engineering, Department of
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Civil Engineering

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