Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28742
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dc.contributor.authorGonzález Vilas, Luisen_UK
dc.contributor.authorSpyrakos, Evangelosen_UK
dc.contributor.authorTorres Palenzuela, Jesus Men_UK
dc.contributor.authorPazos, Yolandaen_UK
dc.date.accessioned2019-02-12T01:04:10Z-
dc.date.available2019-02-12T01:04:10Z-
dc.date.issued2014-05-31en_UK
dc.identifier.urihttp://hdl.handle.net/1893/28742-
dc.description.abstractPhytoplanktonic blooms in the coastal embayments (rias) at the NW part of Spain were mentioned for the first time in 1918 and since then they have been associated numerous times with negatives impacts to a very important economic activity in the area, mussel production. In this study, eight years of Pseudo-nitzschia spp. abundance and associated meteorological and oceanographic data were used to develop and validate support vector machine (SVM) models for the prediction of these diatoms. SVM were used to identify presence/below low detection limit, bloom/no bloom conditions of Pseudo-nitzschia spp. and finally to predict blooms due to these diatoms in the coastal systems of the Galician rias. The best SVM models were selected on the basis of C and γ parameters and their performance was evaluated in terms of accuracy and kappa statistics (κ). Regarding the presence/below low detection limit, bloom/no bloom models the best results in the validation dataset were achieved using all the variables: ria code, day of the year, temperature, salinity, upwelling indices and bloom occurrence in previous weeks. The best performing models were also tested in an independent dataset from the study area, where they showed high overall accuracy (78.53-82.18%), κ values (0.77-0.81) and true positive rates (62.60-78.18). In these models the bloom occurrence in previous weeks was identified as a key parameter to the prediction performance. In this paper, toxic Pseudo-nitzschia blooms could not be predicted due to limited information on toxin concentration and species composition. Nevertheless, this study demonstrates that the approach followed here is capable for high predictive performance which could be of great aid in the monitoring of algal blooms and offer valuable information to the local shellfish industry. The reliable prediction of categorical Pseudo-nitzschia abundances using variables that are operationally determined or short-term predicted could provide early warning of an impending bloom and could help to the development of strategies that could minimize the risks to human health and protect valuable economic resources.en_UK
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.relationGonzález Vilas L, Spyrakos E, Torres Palenzuela JM & Pazos Y (2014) Support Vector Machine-based method for predicting Pseudo-nitzschia spp. blooms in coastal waters (Galician rias, NW Spain). Progress in Oceanography, 124, pp. 66-77. https://doi.org/10.1016/j.pocean.2014.03.003.en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.titleSupport Vector Machine-based method for predicting Pseudo-nitzschia spp. blooms in coastal waters (Galician rias, NW Spain)en_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2999-12-31en_UK
dc.rights.embargoreason[B7_C14 Support vector machine-based method.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1016/j.pocean.2014.03.003en_UK
dc.citation.jtitleProgress in Oceanographyen_UK
dc.citation.issn0079-6611en_UK
dc.citation.volume124en_UK
dc.citation.spage66en_UK
dc.citation.epage77en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailevangelos.spyrakos@stir.ac.uken_UK
dc.citation.date27/03/2014en_UK
dc.contributor.affiliationUniversity of Vigoen_UK
dc.contributor.affiliationUniversity of Vigoen_UK
dc.contributor.affiliationUniversity of Vigoen_UK
dc.contributor.affiliationInstituto Tecnológico para el Control del Medio Marino de Galicia (INTECMAR)en_UK
dc.identifier.isi000336876100006en_UK
dc.identifier.scopusid2-s2.0-84900337641en_UK
dc.identifier.wtid1078887en_UK
dc.date.accepted2014-03-14en_UK
dc.date.firstcompliantdepositdate2018-12-20en_UK
dc.description.refREF Compliant by Deposit in Stirling's Repositoryen_UK
Appears in Collections:Biological and Environmental Sciences Journal Articles

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