{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84103"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84103","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Rapid Estimate of Hurricane Wind, Rain and Storm Surge Under Changing Climate","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Snaiki, Reda; 0000-0003-4326-3655"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Wu, Teng","Civil, Structural and Environmental Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:58Z","date_published":"2022-06-21T15:47:58Z","updated_at":"2026-07-27T19:05:30Z","subjects":["civil engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/84103","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wu, Teng","Civil, Structural and Environmental Engineering"]},{"key":"dc:creator","label":"Author","values":["Snaiki, Reda; 0000-0003-4326-3655"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:58Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["civil engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/84103"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Hurricane-related strong winds, torrential rainfall and devastating storm surge are responsible for significant economic losses and casualties in coastal areas. Mitigation of losses due to hurricane hazards has become an increasingly urgent and challenging problem in light of the changing climate and continued escalation of coastal population density. Accurate and efficient modeling of the hurricane wind, rain and storm surge under changing climate is critical to ensure the safety and reliability of structures subject to these hazards. To this end, both physics-based and/or data-driven reduced-order modeling methodologies are utilized for rapid estimate of hurricane wind, rain and storm surge hazards. More specifically, three types of models based on analytical, semi-empirical and knowledge-enhanced deep learning approaches are developed. The analytical model is derived from the physics-based momentum equations, the semi-empirical model is obtained by fitting the field measurement data, and the knowledge-enhanced deep learning model combines the data-driven machine learning capabilities with prior knowledge in terms of both physics-based equations and/or semi-empirical formulas. The developed hurricane hazard models are then integrated into an improved hurricane risk assessment framework to generate a large synthetic database of full-track storms from the genesis to dissipation stage under observed and projected climate conditions. Accordingly, the hurricane-induced hazard risks are efficiently evaluated at the desired locations.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Rapid Estimate of Hurricane Wind, Rain and Storm Surge Under Changing Climate"]}]}],"canonical_facts":{"dc:contributor":["Wu, Teng","Civil, Structural and Environmental Engineering"],"dc:creator":["Snaiki, Reda; 0000-0003-4326-3655"],"dc:date":["2022-06-21T15:47:58Z","2020"],"dc:description":["Ph.D.","Hurricane-related strong winds, torrential rainfall and devastating storm surge are responsible for significant economic losses and casualties in coastal areas. Mitigation of losses due to hurricane hazards has become an increasingly urgent and challenging problem in light of the changing climate and continued escalation of coastal population density. Accurate and efficient modeling of the hurricane wind, rain and storm surge under changing climate is critical to ensure the safety and reliability of structures subject to these hazards. To this end, both physics-based and/or data-driven reduced-order modeling methodologies are utilized for rapid estimate of hurricane wind, rain and storm surge hazards. More specifically, three types of models based on analytical, semi-empirical and knowledge-enhanced deep learning approaches are developed. The analytical model is derived from the physics-based momentum equations, the semi-empirical model is obtained by fitting the field measurement data, and the knowledge-enhanced deep learning model combines the data-driven machine learning capabilities with prior knowledge in terms of both physics-based equations and/or semi-empirical formulas. The developed hurricane hazard models are then integrated into an improved hurricane risk assessment framework to generate a large synthetic database of full-track storms from the genesis to dissipation stage under observed and projected climate conditions. Accordingly, the hurricane-induced hazard risks are efficiently evaluated at the desired locations.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84103"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["civil engineering"],"dc:title":["Rapid Estimate of Hurricane Wind, Rain and Storm Surge Under Changing Climate"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:30Z"}