Repository logo
 
Publication

A review of recent machine learning advances for forecasting harmful Algal Blooms and shellfish contamination

2021Journal article Open access
Simple item page
dc.contributor.authorCruz, Rafaela C.
dc.contributor.authorReis Costa, Pedro
dc.contributor.authorVinga, Susana
dc.contributor.authorKrippahl, Ludwig
dc.contributor.authorLopes, Marta B.
dc.date.accessioned2021-04-20T08:38:19Z
dc.date.available2021-04-20T08:38:19Z
dc.date.issued2021
dc.description.abstractHarmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities.pt_PT
dc.description.sponsorshipCEECINST/00102/2018/ UIDB/04516/2020/ UIDB/00297/2020/ UIDB/50021/2020/ UID/Multi/04326/2020pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/jmse9030283pt_PT
dc.identifier.issn2077-1312
dc.identifier.urihttp://hdl.handle.net/10400.1/15417
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMarine biotoxinspt_PT
dc.subjectToxic phytoplanktonpt_PT
dc.subjectShellfish productionpt_PT
dc.subjectHarmful algal bloomspt_PT
dc.titleA review of recent machine learning advances for forecasting harmful Algal Blooms and shellfish contaminationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue3pt_PT
oaire.citation.startPage283pt_PT
oaire.citation.titleJournal of Marine Science and Engineeringpt_PT
oaire.citation.volume9pt_PT
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
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication24ad7119-d61f-4bdb-a5d5-9bd6e2c37312
relation.isAuthorOfPublication.latestForDiscovery24ad7119-d61f-4bdb-a5d5-9bd6e2c37312

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
jmse-09-00283-v2.pdf
Size:
463.63 KB
Format:
Adobe Portable Document Format
Download

Collections

CCM2-Artigos (em revistas ou actas indexadas)