Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning

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dc.contributor.authorLee, Jun-Whanen
dc.contributor.authorIrish, Jennifer L.en
dc.contributor.authorBensi, Michelle T.en
dc.contributor.authorMarcy, Douglas C.en
dc.date.accessioned2022-01-31T22:20:55Zen
dc.date.available2022-01-31T22:20:55Zen
dc.date.issued2021-12-01en
dc.date.updated2022-01-31T22:20:50Zen
dc.description.abstractRapid and accurate prediction of peak storm surges across an extensive coastal region is necessary to inform assessments used to design the systems that protect coastal communities’ life and property. Significant advances in high-fidelity, physics-based numerical models have been made in recent years, but use of these models for probabilistic forecasting and probabilistic hazard assessment is computationally intensive. Several surrogate modeling approaches based on existing databases of high-fidelity synthetic storm surge simulations have been recently suggested to reduce computational burden without substantial loss of accuracy. In these previous studies, however, the surrogate modeling approaches relied on a tropical cyclone condition at one moment (usually at or near landfall), which is not always most correlated with the peak storm surge. In this study, a new one-dimensional convolutional neural network model combined with principal component analysis and a k-means clustering (C1PKNet) is presented that can rapidly predict peak storm surge across an extensive coastal region from time-series of tropical cyclone conditions, namely the storm track. The C1PKNet model was trained and cross-validated for the Chesapeake Bay area of the United States using existing database of 1031 high-fidelity storm surge simulations, including both landfalling and bypassing storms. Moreover, the performance of the C1PKNet model was evaluated based on observations from three historical hurricanes (Hurricane Isabel in 2003, Hurricane Irene in 2011, and Hurricane Sandy in 2012). The results indicate that the C1PKNet model is computationally efficient and can predict peak storm surges from realistic tropical cyclone track time-series. We believe that this new surrogate model can enhance coastal resilience by providing rapid storm surge predictions.en
dc.description.versionAccepted versionen
dc.format.extent16 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN 104024 (Article number)en
dc.identifier.doihttps://doi.org/10.1016/j.coastaleng.2021.104024en
dc.identifier.eissn1872-7379en
dc.identifier.issn0378-3839en
dc.identifier.orcidIrish, Jennifer [0000-0002-2429-5953]en
dc.identifier.urihttp://hdl.handle.net/10919/108051en
dc.identifier.volume170en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000707064400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectEngineering, Civilen
dc.subjectEngineering, Oceanen
dc.subjectEngineeringen
dc.subjectStorm surgeen
dc.subjectConvolutional neural networken
dc.subjectPrincipal component analysisen
dc.subjectK-means clusteringen
dc.subjectSurrogate modelingen
dc.subjectChesapeake Bayen
dc.subjectHurricane Isabelen
dc.subjectHurricane Ireneen
dc.subjectHurricane Sandyen
dc.subjectSEA-LEVEL RISEen
dc.subjectCHESAPEAKE BAYen
dc.subjectWAVEen
dc.subjectAPPROXIMATIONen
dc.subjectATTRIBUTESen
dc.subjectMODELen
dc.subjectCOASTen
dc.subjectOceanographyen
dc.subject0403 Geologyen
dc.subject0405 Oceanographyen
dc.subject0905 Civil Engineeringen
dc.titleRapid prediction of peak storm surge from tropical cyclone track time series using machine learningen
dc.title.serialCoastal Engineeringen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Civil & Environmental Engineeringen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciences/Durelle Scotten
pubs.organisational-group/Virginia Tech/Report testen

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