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Piece‐wise constant cluster modelling of dynamics of upwelling patterns

2023-09Journal article Open access
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dc.contributor.authorNascimento, Susana
dc.contributor.authorMartins, Alexandre
dc.contributor.authorRelvas, Paulo
dc.contributor.authorLuis, Joaquim
dc.contributor.authorMirkin, Boris
dc.date.accessioned2023-11-06T14:50:16Z
dc.date.available2023-11-06T14:50:16Z
dc.date.issued2023-09
dc.description.abstractA comprehensive approach is presented to analyse season's coastal upwelling represented by weekly sea surface temperature (SST) image grids. Our three-stage data recovery clustering method assumes that the season's upwelling can be divided into shorter periods of stability, ranges, each to be represented by a constant core and variable shell parts. Corresponding clustering algorithms parameters are automatically derived by using the least-squares clustering criterion. The approach has been successfully applied to real-world SST data covering two distinct regions: Portuguese coast and Morocco coast, for 16 years each.pt_PT
dc.description.sponsorshipLA/P/0101/2020pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1111/exsy.13446pt_PT
dc.identifier.eissn1468-0394
dc.identifier.urihttp://hdl.handle.net/10400.1/20129
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherWileypt_PT
dc.relationAlgarve Centre for Marine Sciences
dc.relationAlgarve Centre for Marine Sciences
dc.relationNOVA Laboratory for Computer Science and Informatics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCoastal upwellingpt_PT
dc.subjectData recovery clusteringspt_PT
dc.subjectSpatiotemporal clusteringpt_PT
dc.subjectSSTpt_PT
dc.subjectImagespt_PT
dc.subjectTime series segmentationpt_PT
dc.titlePiece‐wise constant cluster modelling of dynamics of upwelling patternspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitleNOVA Laboratory for Computer Science and Informatics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT
oaire.citation.issue10pt_PT
oaire.citation.titleExpert Systemspt_PT
oaire.citation.volume40pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameRelvas
person.familyNameLuis
person.givenNamePaulo
person.givenNameJoaquim
person.identifier.ciencia-id2412-1F65-A044
person.identifier.ciencia-id0D11-8EF9-2E68
person.identifier.orcid0000-0002-6404-5895
person.identifier.orcid0000-0002-9035-4069
person.identifier.ridB-1257-2008
person.identifier.ridA-1112-2009
person.identifier.scopus-author-id6505976206
person.identifier.scopus-author-id7006391353
project.funder.identifierhttp://doi.org/10.13039/501100001871
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
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication636a16a1-f50a-49d2-9d5c-dbc3444815bd
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