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Early Warning System development: Quarteira and Praia de Faro

2022Report Restricted
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dc.contributor.authorGarzon, Juan L.
dc.contributor.authorZozimo, Catarina
dc.contributor.authorFerreira, Andreia M. Marques
dc.contributor.authorFerreira, Oscar
dc.contributor.authorFortes, Conceição Juana
dc.contributor.authorReis, Maria Teresa
dc.date.accessioned2022-02-22T11:12:18Z
dc.date.available2022-02-22T11:12:18Z
dc.date.issued2022
dc.description.abstractMany coastal zones worldwide are heavily populated and host very important socio-economic sectors. Portugal is a good example of countries whose economy is highly dependent on tourism activities, especially those sea-related activities. The two sites selected in this project (Quarteira and Faro) receive thousands of national and international visitors annually, not only during the summertime but also in the rest of the seasons because of the favorable weather conditions. However, these sites have been acknowledged as coastal risk hotspots due to their exposure to wave-induced flooding and erosion. Under this threat, the implementation of effective disaster risk reduction (DRR) plans is vital for minimizing damages in occupied areas. In this regard, Early Warning Systems (EWSs) play an important role in allowing for preparedness, namely, timely site evacuation or effective intervention prior to the approaching storm. The successful implementation of EWSs is one of the most cost-effective and efficient measures for disaster risk reduction and the saving of lives. EWSs can rely on complex tools such as process-based models to simulate coastal hazards namely erosion and flooding. However, they are normally highly time-consuming and this aspect might represent a major limitation for operational systems. Conversely, Bayesian Networks (BNs) can provide risk probabilities instantly after being trained and they have been successfully used to make predictions of storm impacts in several coastal applications. The main disadvantage of Bayesian Networks is that they are data-intensive, requiring large input information in order to derive the probabilistic relationships used in their predictions. Under the lack of field observations, process-based models can be used to generate this required information. Once trained, the BN can be used as a surrogate for a process-based model in an EWS.pt_PT
dc.description.sponsorshipALG-LISBOA-01-145-FEDER- 028657
dc.description.versioninfo:eu-repo/semantics/submittedVersionpt_PT
dc.identifier.doi10.34623/bdcs-3z27
dc.identifier.urihttp://hdl.handle.net/10400.1/17582
dc.language.isoengpt_PT
dc.peerreviewednopt_PT
dc.publisherUAlg - CIMA; LNECpt_PT
dc.relationCentre for Marine and Environmental Research (CIMA)
dc.relation.publisherversionhttps://www.cima.ualg.pt/EW-COAST/pt_PT
dc.titleEarly Warning System development: Quarteira and Praia de Faropt_PT
dc.typereport
dspace.entity.typePublication
oaire.awardTitleCentre for Marine and Environmental Research (CIMA)
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00350%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
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.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typereportpt_PT
relation.isAuthorOfPublicationf54b4900-9c75-4882-9375-908eca195474
relation.isAuthorOfPublication14adf536-4f81-42de-b00f-6d1cf3b0d75e
relation.isAuthorOfPublication.latestForDiscoveryf54b4900-9c75-4882-9375-908eca195474
relation.isProjectOfPublication62b4d568-8b21-464c-97ed-de826eab4136
relation.isProjectOfPublication.latestForDiscovery62b4d568-8b21-464c-97ed-de826eab4136

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