Repository logo
 
Publication

Forecasting bivalve landings with multiple regression and data mining techniques: The case of the Portuguese Artisanal Dredge Fleet

2017-10Journal article Restricted access
Simple item page
dc.contributor.authorOliveira, Manuela M.
dc.contributor.authorCamanho, Ana S.
dc.contributor.authorWalden, John B.
dc.contributor.authorMigueis, Vera L.
dc.contributor.authorFerreira, Nuno B.
dc.contributor.authorGaspar, Miguel
dc.date.accessioned2019-11-20T15:07:18Z
dc.date.available2019-11-20T15:07:18Z
dc.date.issued2017-10
dc.description.abstractThis paper develops a decision support tool that can help fishery authorities to forecast bivalve landings for the dredge fleet accounting for several contextual conditions. These include weather conditions, phytotoxins episodes, stock-biomass indicators per species and tourism levels. Vessel characteristics and fishing effort are also taken into account for the estimation of landings. The relationship between these factors and monthly quantities landed per vessel is explored using multiple linear regression models and data mining techniques (random forests, support vector machines and neural networks). The models are specified for different regions in the Portugal mainland (Northwest, Southwest and South) using six years of data 2010-2015). Results showed that the impact of the contextual factors varies between regions and also depends on the vessels target species. The data mining techniques, namely the random forests, proved to be a robust decision support tool in this context, outperforming the predictive performance of the most popular technique used in this context, i.e. linear regression.
dc.description.sponsorshipFoundation for Science and Technology (FCT, Portugal) [SFRH/BPD/99570/2014]
dc.description.sponsorshipERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE Programme [POCI-01-0145-FEDER-006961]
dc.description.sponsorshipNational Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) [UID/EEA/50014/2013]
dc.description.sponsorshipproject MONTEREAL
dc.description.sponsorshipMAR Program
dc.description.sponsorshipEuropean fund for Fisheries and Maritime Affairs (EFFM)
dc.description.sponsorshipPortuguese Government
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1016/j.marpol.2017.07.013
dc.identifier.issn0308-597X
dc.identifier.issn1872-9460
dc.identifier.urihttp://hdl.handle.net/10400.1/12976
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectNeural-Networks
dc.subjectFish
dc.subjectClassification
dc.subjectPerformance
dc.subjectTexture
dc.subjectCoast
dc.titleForecasting bivalve landings with multiple regression and data mining techniques: The case of the Portuguese Artisanal Dredge Fleet
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F50014%2F2013/PT
oaire.citation.endPage118
oaire.citation.startPage110
oaire.citation.titleMarine Policy
oaire.citation.volume84
oaire.fundingStream5876
person.familyNameGaspar
person.givenNameMiguel
person.identifier.ciencia-idF719-07AC-E41F
person.identifier.orcid0000-0001-9245-8518
person.identifier.ridF-5398-2011
person.identifier.scopus-author-id23501123500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccess
rcaap.typearticle
relation.isAuthorOfPublication6185b7ee-acc9-4a7e-a7db-37384e94f4df
relation.isAuthorOfPublication.latestForDiscovery6185b7ee-acc9-4a7e-a7db-37384e94f4df
relation.isProjectOfPublicationec900b54-6dc6-4e85-9c86-d36ac115196f
relation.isProjectOfPublication.latestForDiscoveryec900b54-6dc6-4e85-9c86-d36ac115196f

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
1-s2.0-S0308597X17303159-main.pdf
Size:
540.13 KB
Format:
Adobe Portable Document Format
Download

Collections

CCM2-Artigos (em revistas ou actas indexadas)