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Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks

2022-12Journal article Open access
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dc.contributor.authorCruz, Rafaela C.
dc.contributor.authorReis Costa, Pedro
dc.contributor.authorKrippahl, Ludwig
dc.contributor.authorLopes, Marta B.
dc.date.accessioned2023-01-20T14:05:52Z
dc.date.available2023-01-20T14:05:52Z
dc.date.issued2022-12
dc.description.abstractHarmful algal blooms (HABs) and the consequent contamination of shellfish are complex processes depending on several biotic and abiotic variables, turning prediction of shellfish contamination into a challenging task. Not only the information of interest is dispersed among multiple sources, but also the complex temporal relationships between the time-series variables require advanced machine methods to model such relationships. In this study, multiple time-series variables measured in Portuguese shellfish production areas were used to forecast shellfish contamination by diarrhetic she-llfish poisoning (DSP) toxins one to four weeks in advance. These time series included DSP con-centration in mussels (Mytilus galloprovincialis), toxic phytoplankton cell counts, meteorological, and remotely sensed oceanographic variables. Several data pre-processing and feature engineering methods were tested, as well as multiple autoregressive and artificial neural network (ANN) models. The best results regarding the mean absolute error of prediction were obtained for a bivariate long short-term memory (LSTM) neural network based on biotoxin and toxic phytoplankton measurements, with higher accuracy for short-term forecasting horizons. When evaluating all ANNs model ability to predict the contamination state (below or above the regulatory limit for contamination) and changes to this state, multilayer perceptrons (MLP) and convolutional neural networks (CNN) yielded improved predictive performance on a case-by-case basis. These results show the possibility of extracting relevant information from time-series data from multiple sources which are predictive of DSP contamination in mussels, therefore placing ANNs as good candidate models to assist the production sector in anticipating harvesting interdictions and mitigating economic losses.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.knosys.2022.109895pt_PT
dc.identifier.eissn1872-7409
dc.identifier.urihttp://hdl.handle.net/10400.1/18877
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationA machine learning-based forecasting system for shellfish safety
dc.relationNot Available
dc.relationNOVA Laboratory for Computer Science and Informatics
dc.relationCenter for Mathematics and Applications
dc.relationCenter for Mathematics and Applications
dc.relationAlgarve Centre for Marine Sciences
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectTime seriespt_PT
dc.subjectForecastingpt_PT
dc.subjectArtificial Neural Networkspt_PT
dc.subjectBiotoxinspt_PT
dc.subjectShellfish contaminationpt_PT
dc.subjectHarmful algal bloomspt_PT
dc.titleForecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networkspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleA machine learning-based forecasting system for shellfish safety
oaire.awardTitleNot Available
oaire.awardTitleNOVA Laboratory for Computer Science and Informatics
oaire.awardTitleCenter for Mathematics and Applications
oaire.awardTitleCenter for Mathematics and Applications
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0026%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC INST 2018/CEECINST%2F00102%2F2018%2FCP1567%2FCT0001/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00297%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00297%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04326%2F2020/PT
oaire.citation.startPage109895pt_PT
oaire.citation.titleKnowledge-Based Systemspt_PT
oaire.citation.volume257pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStreamCEEC INST 2018
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameReis Costa
person.givenNamePedro
person.identifier600820
person.identifier.ciencia-idC911-9715-E547
person.identifier.orcid0000-0001-6083-470X
person.identifier.ridN-1908-2019
person.identifier.scopus-author-id7201895802
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.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
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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