{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110461"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110461","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Investigation of climate change impact on hurricane wind and freshwater flood risks using machine learning techniques","abstract":"Hurricane causes severe damage along with the U.S. coastal states. With the potential increase in hurricane intensity in changing climate conditions, the impacts of hurricanes are expected to be severer. Current hurricane risk management practices are based on the hurricane risk assessment without considering climate impact, which would result in a higher level of risk for the built environment than intended. For the development of proper hurricane risk management strategies, it is crucial to investigate the climate change impact on hurricane risk. However, investigation of future hurricane risk can be very time-consuming because of the high resolution of the models for climate-dependent hazard simulation and regional loss assessment. This study aims at investigating the climate change impact on hurricane wind and rain-ingress risk and freshwater flood risk on residential buildings across the southeastern U.S. coastal states. To address the challenge of computational inefficiency, surrogate models are developed using machine learning techniques for evaluating wind and freshwater flood losses of simulated climate-dependent hurricane scenarios. It is found that climate change impact varies by region and has a more significant influence on wind and rain-ingress damage, while both increases in wind and flood risks are not negligible.","abstract_html":"Hurricane causes severe damage along with the U.S. coastal states. With the potential increase in hurricane intensity in changing climate conditions, the impacts of hurricanes are expected to be severer. Current hurricane risk management practices are based on the hurricane risk assessment without considering climate impact, which would result in a higher level of risk for the built environment than intended. For the development of proper hurricane risk management strategies, it is crucial to investigate the climate change impact on hurricane risk. However, investigation of future hurricane risk can be very time-consuming because of the high resolution of the models for climate-dependent hazard simulation and regional loss assessment. This study aims at investigating the climate change impact on hurricane wind and rain-ingress risk and freshwater flood risk on residential buildings across the southeastern U.S. coastal states. To address the challenge of computational inefficiency, surrogate models are developed using machine learning techniques for evaluating wind and freshwater flood losses of simulated climate-dependent hurricane scenarios. It is found that climate change impact varies by region and has a more significant influence on wind and rain-ingress damage, while both increases in wind and flood risks are not negligible.","abstract_has_math":false,"creators":["Lin, Chi-Ying"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Cha, Eun Jeong","Gardoni, Paolo","Wang, Zhuo","Zhu, Ruoqing"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T01:10:47Z","date_published":"2021-09-17T01:10:47Z","updated_at":"2026-07-22T22:24:50Z","subjects":["Artificial neural network","Climate change","Wind","Freshwater flooding","Hurricane","Residential buildings","Risk assessment"],"languages":["en"],"rights":["Copyright 2021 Chi-Ying Lin"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110461","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Cha, Eun Jeong","Gardoni, Paolo","Wang, Zhuo","Zhu, Ruoqing"]},{"key":"dc:creator","label":"Author","values":["Lin, Chi-Ying"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T01:10:47Z","2021-04-15","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial neural network","Climate change","Wind","Freshwater flooding","Hurricane","Residential buildings","Risk assessment"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Chi-Ying Lin"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110461"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Hurricane causes severe damage along with the U.S. coastal states. With the potential increase in hurricane intensity in changing climate conditions, the impacts of hurricanes are expected to be severer. Current hurricane risk management practices are based on the hurricane risk assessment without considering climate impact, which would result in a higher level of risk for the built environment than intended. For the development of proper hurricane risk management strategies, it is crucial to investigate the climate change impact on hurricane risk. However, investigation of future hurricane risk can be very time-consuming because of the high resolution of the models for climate-dependent hazard simulation and regional loss assessment. This study aims at investigating the climate change impact on hurricane wind and rain-ingress risk and freshwater flood risk on residential buildings across the southeastern U.S. coastal states. To address the challenge of computational inefficiency, surrogate models are developed using machine learning techniques for evaluating wind and freshwater flood losses of simulated climate-dependent hurricane scenarios. It is found that climate change impact varies by region and has a more significant influence on wind and rain-ingress damage, while both increases in wind and flood risks are not negligible.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Chi-Ying Lin, accepted the attached license on 2021-04-12 at 14:38.","The student, Chi-Ying Lin, submitted this Dissertation for approval on 2021-04-12 at 15:04.","This Dissertation was approved for publication on 2021-04-15 at 15:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16291 on 2021-09-16 at 16:41:02","Made available in DSpace on 2021-09-17T01:10:47Z (GMT). No. of bitstreams: 3 LIN-DISSERTATION-2021.pdf: 2555107 bytes, checksum: b8bc5a4bb45051de9a8696d5cfad5764 (MD5) LICENSE.txt: 4209 bytes, checksum: 67cc598705f33b2c15b0ae581dc76d00 (MD5) PROQUEST_LICENSE.txt: 4555 bytes, checksum: f9175abf373b77117fa0ca5d440ef973 (MD5) Previous issue date: 2021-04-15"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Investigation of climate change impact on hurricane wind and freshwater flood risks using machine learning techniques"]}]}],"canonical_facts":{"dc:contributor":["Cha, Eun Jeong","Gardoni, Paolo","Wang, Zhuo","Zhu, Ruoqing"],"dc:creator":["Lin, Chi-Ying"],"dc:date":["2021-09-17T01:10:47Z","2021-04-15","2021-05"],"dc:description":["Hurricane causes severe damage along with the U.S. coastal states. With the potential increase in hurricane intensity in changing climate conditions, the impacts of hurricanes are expected to be severer. Current hurricane risk management practices are based on the hurricane risk assessment without considering climate impact, which would result in a higher level of risk for the built environment than intended. For the development of proper hurricane risk management strategies, it is crucial to investigate the climate change impact on hurricane risk. However, investigation of future hurricane risk can be very time-consuming because of the high resolution of the models for climate-dependent hazard simulation and regional loss assessment. This study aims at investigating the climate change impact on hurricane wind and rain-ingress risk and freshwater flood risk on residential buildings across the southeastern U.S. coastal states. To address the challenge of computational inefficiency, surrogate models are developed using machine learning techniques for evaluating wind and freshwater flood losses of simulated climate-dependent hurricane scenarios. It is found that climate change impact varies by region and has a more significant influence on wind and rain-ingress damage, while both increases in wind and flood risks are not negligible.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Chi-Ying Lin, accepted the attached license on 2021-04-12 at 14:38.","The student, Chi-Ying Lin, submitted this Dissertation for approval on 2021-04-12 at 15:04.","This Dissertation was approved for publication on 2021-04-15 at 15:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16291 on 2021-09-16 at 16:41:02","Made available in DSpace on 2021-09-17T01:10:47Z (GMT). No. of bitstreams: 3 LIN-DISSERTATION-2021.pdf: 2555107 bytes, checksum: b8bc5a4bb45051de9a8696d5cfad5764 (MD5) LICENSE.txt: 4209 bytes, checksum: 67cc598705f33b2c15b0ae581dc76d00 (MD5) PROQUEST_LICENSE.txt: 4555 bytes, checksum: f9175abf373b77117fa0ca5d440ef973 (MD5) Previous issue date: 2021-04-15"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/110461"],"dc:language":["en"],"dc:rights":["Copyright 2021 Chi-Ying Lin"],"dc:subject":["Artificial neural network","Climate change","Wind","Freshwater flooding","Hurricane","Residential buildings","Risk assessment"],"dc:title":["Investigation of climate change impact on hurricane wind and freshwater flood risks using machine learning techniques"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:50Z"}