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Development of a Bayesian networks-based early warning system for wave-induced flooding

dc.contributor.authorGarzon, Juan L.
dc.contributor.authorFerreira, Óscar
dc.contributor.authorZózimo, A. C.
dc.contributor.authorFortes, C. J. E. M.
dc.contributor.authorFerreira, A. M.
dc.contributor.authorPinheiro, L. V.
dc.contributor.authorReis, M. T.
dc.date.accessioned2023-11-03T14:58:15Z
dc.date.available2023-11-03T14:58:15Z
dc.date.issued2023-10
dc.description.abstractCoastal flooding prediction systems can be an efficient risk-reduction instrument. The goal of this study was to design, build, test, and implement a wave-induced flooding early warning system in urban areas fronted by sandy beaches. The system utilizes a novel approach that combines Bayesian Networks and numerical models (SWAN + XBeach) and was developed in two phases. In the development phase, firstly, the learning information was generated including the creation of oceanic conditions, modeling overtopping discharges, the characterization of the associated impacts (no, low, moderate and high) in pedestrians, urban components and buildings, and vehicles, and secondly, the Bayesian Networks were designed that surrogated the previously generated information. After their training, the conditional probability tables were created representing the foundation to make predictions in the operational phase. This methodology was validated for several historical events which hit the study area (Praia de Faro, Portugal), and the system correctly predicted the impact level of around 80% of the cases. Also, the predictive skills varied depending on the level, with the no and high impact levels overcoming the intermediate levels. In terms of efficiency, one simulation (deterministic) of coastal flooding for 72 h by running SWAN + XBeach operationally would take more than two days on a one-logical processor workstation, while the current approach can provide quasi-instantaneously predictions for that period, including probability distributions. Moreover, the two-working phase approach is very flexible enabling the inclusion of additional features such as social components representing a powerful tool for risk reduction in coastal communities.pt_PT
dc.description.sponsorshipLA/P/0069/2020, research projects EW-COAST ALG-LISBOA-01-145-FEDER-028657pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.ijdrr.2023.103931pt_PT
dc.identifier.issn2212-4209
dc.identifier.urihttp://hdl.handle.net/10400.1/20122
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationCentre for Marine and Environmental Research (CIMA)
dc.relationTo-SEAlert - Wave overtopping and flooding in coastal and port areas: Tools for an early warning, emergency planning and risk management system.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectPrediction systempt_PT
dc.subjectBeachpt_PT
dc.subjectBayesian networkpt_PT
dc.subjectSandy beachespt_PT
dc.subjectWave overtoppingpt_PT
dc.titleDevelopment of a Bayesian networks-based early warning system for wave-induced floodingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for Marine and Environmental Research (CIMA)
oaire.awardTitleTo-SEAlert - Wave overtopping and flooding in coastal and port areas: Tools for an early warning, emergency planning and risk management system.
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00350%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEAM-OCE%2F31207%2F2017/PT
oaire.citation.startPage103931pt_PT
oaire.citation.titleInternational Journal of Disaster Risk Reductionpt_PT
oaire.citation.volume96pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
person.familyNameGarzon
person.familyNameFerreira
person.givenNameJuan L.
person.givenNameÓscar
person.identifier.ciencia-idCB1A-D085-6C56
person.identifier.ciencia-id1F1C-DF44-94C9
person.identifier.orcid0000-0001-7641-4144
person.identifier.orcid0000-0001-9975-0036
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication14adf536-4f81-42de-b00f-6d1cf3b0d75e
relation.isAuthorOfPublication.latestForDiscovery14adf536-4f81-42de-b00f-6d1cf3b0d75e
relation.isProjectOfPublication62b4d568-8b21-464c-97ed-de826eab4136
relation.isProjectOfPublicationd5018b00-8c05-4604-be53-9850ce6e738b
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