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dc.contributor.authorFeki-Sahnoun, Wafa
dc.contributor.authorNjah, Hasna
dc.contributor.authorHamza, Asma
dc.contributor.authorBarraj, Nouha
dc.contributor.authorMahfoudi, Mabrouka
dc.contributor.authorRebai, Ahmed
dc.contributor.authorBelhassen, Malika
dc.coverage.spatialGulf of Gabes, Tunisiaen_US
dc.date.accessioned2018-04-16T15:24:42Z
dc.date.available2018-04-16T15:24:42Z
dc.date.issued2018
dc.identifier.doi10.1016/j.ecoinf.2017.10.017
dc.identifier.urihttp://hdl.handle.net/1834/12526
dc.description.abstractThe prediction of the dinoflagellate red tide forming Karenia selliformis is a relevant task to aid optimized management decisions in marine coastal water. The objective of the present study is to compare different modeling approaches for prediction of Karenia selliformis occurrences and blooms. A set of physical parameters (salinity, temperature and tide amplitude), meteorological constraints (evaporation, air temperature, insolation, rainfall, atmospheric pressure and humidity), sampling months and sampling sites are used. The model prediction included General Linear Model (GLM), Bayesian Network (BN) and the simplest BN type which is, Naive Bayes classifier (NB). The results showed that three models incriminated high salinity in Karenia selliformis blooms and the sampling sites, mainly Boughrara lagoon, in the occurrences. The BN performed better than linear models (NB and GLM) for both Karenia selliformis occurrences and blooms prediction. This later is related to the facts that BN considered the inter-independency between predictive variables and that the relationships between the variables and the outcome are often non-linear such us; the transition to bloom situations appeared to be triggered by a salinity threshold. This study is useful in the management of this ecosystem so as to use the best disposal options in the early prediction of the toxic blooms.en_US
dc.language.isoenen_US
dc.relation.urihttps://www.sciencedirect.com/journal/ecological-informaticsen_US
dc.subject.otherKarenia selliformis.en_US
dc.subject.otherNaive Bayes classifier.en_US
dc.subject.otherGeneral linear model.en_US
dc.subject.otherBayesian Network.en_US
dc.subject.otherHydro-meteorological parameters.en_US
dc.titleUsing General linear model, Bayesian networks and Naive Bayes classifier for prediction of Karenia selliformis occurrences and blooms.en_US
dc.typeJournal Contributionen_US
dc.bibliographicCitation.titleEcological Informatics [In Press]en_US
dc.bibliographicCitation.volume43en_US
dc.description.statusIn Pressen_US
dc.format.pagerangepp.12-23en_US
dc.type.refereedNon Refereeden_US
refterms.dateFOA2021-01-30T18:47:48Z


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