{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T03:57:41Z","timestamp":1790999861099,"version":"4.1.0"},"reference-count":83,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    In the residential sector, hurricane winds are a major contributor to storm\u2010related losses, with substantial annual costs to the US economy. With the potential increase in hurricane intensity in changing climate conditions, hurricane impacts are expected to worsen. 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 expected. It is crucial to investigate the impact of climate change on hurricane risk to develop effective hurricane risk management strategies. However, investigation of future hurricane risk can be very time\u2010consuming because of the high resolution of the models for climate\u2010dependent hazard simulation and regional loss assessment. This study aims to investigate the climate change impact on hurricane wind risk on residential buildings across the southeastern US coastal states. To address the challenge of computational inefficiency, we develop surrogate models using machine learning techniques for evaluating wind and rain\u2010ingress losses of simulated climate\u2010dependent hurricane scenarios. We collect historical hurricane data and use selected climate variables to predict changing hurricane attributes under climate change. We build the surrogate loss model using data generated by the existing fragility\u2010based loss model. The loss estimation of synthetic events using the surrogate model shows an accuracy with a 0.78\n                    <jats:italic>R<\/jats:italic>\n                    \u2010squared value compared to Hazard U.S. \u2013 Multi Hazard (HAZUS\u2010MH) estimation. The results demonstrate the feasibility of utilizing surrogate models to predict risk changes and underline the increasing hurricane wind risk due to climate change.\n                  <\/jats:p>","DOI":"10.1111\/risa.70042","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T22:04:14Z","timestamp":1747865054000},"page":"4378-4396","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evaluating the impact of climate change on hurricane wind risk: A machine learning approach"],"prefix":"10.1111","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7337-5071","authenticated-orcid":false,"given":"Chi\u2010Ying","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Civil Engineering National Yang Ming Chiao Tung University  Hsinchu Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun Jeong","family":"Cha","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering University of Illinois at Urbana\u2010Champaign  Urbana Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,5,21]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adf0259"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1180568"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600\u20100870.2007.00251.x"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2017.11.003"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010019\u201008471\u2010z"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/15732479.2013.816973"},{"key":"e_1_2_7_8_1","unstructured":"CBO. (2019).Expected costs of damage from hurricane winds and storm\u2010related flooding(Issue April).www.cbo.gov\/publication\/55019"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/qj.2374"},{"key":"e_1_2_7_10_1","volume-title":"Predicting the vulnerability of typical residential buildings to hurricane damage","author":"Cope A. D.","year":"2004"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/nclimate1452"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00382\u2010017\u20103636\u20107"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature07234"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS\u201089\u20105\u2010Emanuel"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)NH.1527\u20106996.0000458"},{"key":"e_1_2_7_16_1","unstructured":"FEMA. (2013).Hazus\u2014MH 2.1 technical manual: Hurricane model.https:\/\/www.fema.gov\/sites\/default\/files\/2020\u201009\/fema_hazus_hurricane\u2010model_technical\u2010manual_2.1.pdf"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10584\u2010020\u201002692\u20108"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/joc.4775"},{"key":"e_1_2_7_19_1","unstructured":"Freimarck G.(2022).Incorporating climate change into a hurricane model.https:\/\/www.ara.com\/wp\u2010content\/uploads\/ARA_ClimateChange_NatCatModels_WhitePaper.pdf"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167\u20109473(01)00065\u20102"},{"key":"e_1_2_7_22_1","first-page":"1050","volume-title":"Proceedings of the 33rd International Conference on Machine Learning","author":"Gal Y.","year":"2016"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/atmos13060896"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)ST.1943\u2010541X.0002366"},{"key":"e_1_2_7_25_1","unstructured":"Gurley K. Pinelli J. P. Subramanian C. Cope A. Zhang L. Murphree J. &Artiles A.(2005).Development calibration and validation of vulnerability matrices of the Florida Public Hurricane Loss Projection Model. Florida Public Hurricane Loss Projection Model (FPHLPM) engineering team final report: Vol. III."},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1175\/2007JAMC1532.1"},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1175\/2008MWR2395.1"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00382\u2010013\u20101713\u20100"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.1175\/JCLI\u2010D\u201016\u20100836.1"},{"key":"e_1_2_7_31_1","unstructured":"Hyndman R. 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