{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/832"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/832","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"PREDICTIONS OF SIGNIFICANT WAVE HEIGHT IN LAKE OKEECHOBEE, FLORIDA USING APPROACHES RELATED TO SIMPLIFIED STOCHASTIC PROCEDURE AND WAVE ENERGY SPECTRUM","abstract":"Prediction 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.","abstract_html":"Prediction 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.","abstract_has_math":false,"creators":["Khan, Ismat 1984-"],"institution":"University of Houston","degree_name":"Master of Science in Civil Engineering","degree_level":"Masters","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Wang, Keh-Han"],"committee_chairs":[],"committee_members":["Rogers, Jerry R.","Strom, Kyle B.","Altunkaynak, Abdüsselam"],"year":2012,"date_issued":"2012-12","date_published":"2012-12","updated_at":"2026-07-24T02:32:27Z","subjects":["Significant wave height","Regression Method","Perceptron Least Square Method","Pierson-Moskowitz Spectrum","Artificial neural networks"],"languages":["eng"],"rights":["The 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)."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10657/832","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Wang, Keh-Han"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Rogers, Jerry R.","Strom, Kyle B.","Altunkaynak, Abdüsselam"]},{"key":"dc:creator","label":"Author","values":["Khan, Ismat 1984-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-12-19T13:19:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-12-19T13:19:22Z"]},{"key":"dc:date.issued","label":"Date","values":["2012-12"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Civil Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Significant wave height","Regression Method","Perceptron Least Square Method","Pierson-Moskowitz Spectrum","Artificial neural networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["The 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)."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10657/832"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Prediction 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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["PREDICTIONS OF SIGNIFICANT WAVE HEIGHT IN LAKE OKEECHOBEE, FLORIDA USING APPROACHES RELATED TO SIMPLIFIED STOCHASTIC PROCEDURE AND WAVE ENERGY SPECTRUM"]}]}],"canonical_facts":{"dc:contributor.advisor":["Wang, Keh-Han"],"dc:contributor.committeemember":["Rogers, Jerry R.","Strom, Kyle B.","Altunkaynak, Abdüsselam"],"dc:creator":["Khan, Ismat 1984-"],"dc:date.accessioned":["2014-12-19T13:19:22Z"],"dc:date.available":["2014-12-19T13:19:22Z"],"dc:date.issued":["2012-12"],"dc:description.abstract":["Prediction 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:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/10657/832"],"dc:language.iso":["eng"],"dc:rights":["The 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:subject":["Significant wave height","Regression Method","Perceptron Least Square Method","Pierson-Moskowitz Spectrum","Artificial neural networks"],"dc:title":["PREDICTIONS OF SIGNIFICANT WAVE HEIGHT IN LAKE OKEECHOBEE, FLORIDA USING APPROACHES RELATED TO SIMPLIFIED STOCHASTIC PROCEDURE AND WAVE ENERGY SPECTRUM"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science in Civil Engineering"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:32:27Z"}