A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination

Detalhes bibliográficos
Autor(a) principal: Cruz, Rafaela C.
Data de Publicação: 2021
Outros Autores: Costa, Pedro Reis, Vinga, Susana, Krippahl, Ludwig, Lopes, Marta B.
Tipo de documento: Outros
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/125029
Resumo: Funding: This work was funded by the project “MATISSE: A machine learning-based forecasting system for shellfish safety” (DSAIPA/DS/0026/2019). The work was also supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018, UIDB/04516/2020 (NOVA LINCS), UIDB/00297/2020 (CMA), UIDB/50021/2020 (INESC-ID), and UID/Multi/04326/2020 (CCMAR).
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spelling A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contaminationArtificial neural networksHarmful algal bloomsMachine learningMarine biotoxinsMultivariate time seriesShellfish productionTime-series forecastingToxic phytoplanktonCivil and Structural EngineeringWater Science and TechnologyOcean EngineeringSDG 13 - Climate ActionSDG 14 - Life Below WaterFunding: This work was funded by the project “MATISSE: A machine learning-based forecasting system for shellfish safety” (DSAIPA/DS/0026/2019). The work was also supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018, UIDB/04516/2020 (NOVA LINCS), UIDB/00297/2020 (CMA), UIDB/50021/2020 (INESC-ID), and UID/Multi/04326/2020 (CCMAR).Harmful 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.DI - Departamento de InformáticaNOVALincsCMA - Centro de Matemática e AplicaçõesRUNCruz, Rafaela C.Costa, Pedro ReisVinga, SusanaKrippahl, LudwigLopes, Marta B.2021-09-23T01:04:34Z2021-03-052021-03-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/otherapplication/pdfhttp://hdl.handle.net/10362/125029eng2077-1312PURE: 33335467https://doi.org/10.3390/jmse9030283info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:56:20Zoai:run.unl.pt:10362/125029Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:27:32.144991Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination
title A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination
spellingShingle A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination
Cruz, Rafaela C.
Artificial neural networks
Harmful algal blooms
Machine learning
Marine biotoxins
Multivariate time series
Shellfish production
Time-series forecasting
Toxic phytoplankton
Civil and Structural Engineering
Water Science and Technology
Ocean Engineering
SDG 13 - Climate Action
SDG 14 - Life Below Water
title_short A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination
title_full A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination
title_fullStr A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination
title_full_unstemmed A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination
title_sort A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination
author Cruz, Rafaela C.
author_facet Cruz, Rafaela C.
Costa, Pedro Reis
Vinga, Susana
Krippahl, Ludwig
Lopes, Marta B.
author_role author
author2 Costa, Pedro Reis
Vinga, Susana
Krippahl, Ludwig
Lopes, Marta B.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv DI - Departamento de Informática
NOVALincs
CMA - Centro de Matemática e Aplicações
RUN
dc.contributor.author.fl_str_mv Cruz, Rafaela C.
Costa, Pedro Reis
Vinga, Susana
Krippahl, Ludwig
Lopes, Marta B.
dc.subject.por.fl_str_mv Artificial neural networks
Harmful algal blooms
Machine learning
Marine biotoxins
Multivariate time series
Shellfish production
Time-series forecasting
Toxic phytoplankton
Civil and Structural Engineering
Water Science and Technology
Ocean Engineering
SDG 13 - Climate Action
SDG 14 - Life Below Water
topic Artificial neural networks
Harmful algal blooms
Machine learning
Marine biotoxins
Multivariate time series
Shellfish production
Time-series forecasting
Toxic phytoplankton
Civil and Structural Engineering
Water Science and Technology
Ocean Engineering
SDG 13 - Climate Action
SDG 14 - Life Below Water
description Funding: This work was funded by the project “MATISSE: A machine learning-based forecasting system for shellfish safety” (DSAIPA/DS/0026/2019). The work was also supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018, UIDB/04516/2020 (NOVA LINCS), UIDB/00297/2020 (CMA), UIDB/50021/2020 (INESC-ID), and UID/Multi/04326/2020 (CCMAR).
publishDate 2021
dc.date.none.fl_str_mv 2021-09-23T01:04:34Z
2021-03-05
2021-03-05T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/other
format other
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/125029
url http://hdl.handle.net/10362/125029
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2077-1312
PURE: 33335467
https://doi.org/10.3390/jmse9030283
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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