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

Bibliographic Details
Main Author: Cruz, Rafaela C.
Publication Date: 2022
Other Authors: Costa, Pedro R., Krippahl, Ludwig, Lopes, Marta B.
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/150296
Summary: Funding Information: The work was also supported by national funds through Fundação para a Ciência e a Tecnologia (FCT), Portugal with references CEECINST/00102/2018 and UID/Multi/04326/2020 (CCMAR). Publisher Copyright: © 2022 The Author(s)
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spelling Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural NetworksArtificial Neural NetworksBiotoxinsForecastingHarmful algal bloomsShellfish contaminationTime seriesSoftwareManagement Information SystemsInformation Systems and ManagementArtificial IntelligenceFunding Information: The work was also supported by national funds through Fundação para a Ciência e a Tecnologia (FCT), Portugal with references CEECINST/00102/2018 and UID/Multi/04326/2020 (CCMAR). Publisher Copyright: © 2022 The Author(s)Harmful 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 shellfish poisoning (DSP) toxins one to four weeks in advance. These time series included DSP concentration 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.DI - Departamento de InformáticaNOVALincsCMA - Centro de Matemática e AplicaçõesRUNCruz, Rafaela C.Costa, Pedro R.Krippahl, LudwigLopes, Marta B.2023-03-09T22:28:55Z2022-12-052022-12-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttp://hdl.handle.net/10362/150296eng0950-7051PURE: 55194415https://doi.org/10.1016/j.knosys.2022.109895info: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:RCAAP2024-05-22T18:09:48Zoai:run.unl.pt:10362/150296Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:40:08.691838Repositórios 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 Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks
title Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks
spellingShingle Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks
Cruz, Rafaela C.
Artificial Neural Networks
Biotoxins
Forecasting
Harmful algal blooms
Shellfish contamination
Time series
Software
Management Information Systems
Information Systems and Management
Artificial Intelligence
title_short Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks
title_full Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks
title_fullStr Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks
title_full_unstemmed Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks
title_sort Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks
author Cruz, Rafaela C.
author_facet Cruz, Rafaela C.
Costa, Pedro R.
Krippahl, Ludwig
Lopes, Marta B.
author_role author
author2 Costa, Pedro R.
Krippahl, Ludwig
Lopes, Marta B.
author2_role 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 R.
Krippahl, Ludwig
Lopes, Marta B.
dc.subject.por.fl_str_mv Artificial Neural Networks
Biotoxins
Forecasting
Harmful algal blooms
Shellfish contamination
Time series
Software
Management Information Systems
Information Systems and Management
Artificial Intelligence
topic Artificial Neural Networks
Biotoxins
Forecasting
Harmful algal blooms
Shellfish contamination
Time series
Software
Management Information Systems
Information Systems and Management
Artificial Intelligence
description Funding Information: The work was also supported by national funds through Fundação para a Ciência e a Tecnologia (FCT), Portugal with references CEECINST/00102/2018 and UID/Multi/04326/2020 (CCMAR). Publisher Copyright: © 2022 The Author(s)
publishDate 2022
dc.date.none.fl_str_mv 2022-12-05
2022-12-05T00:00:00Z
2023-03-09T22:28:55Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/150296
url http://hdl.handle.net/10362/150296
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0950-7051
PURE: 55194415
https://doi.org/10.1016/j.knosys.2022.109895
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 12
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
_version_ 1833596877008797696

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