A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination
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 hea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/9/3/283 |
| _version_ | 1797413508797693952 |
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| author | Rafaela C. Cruz Pedro Reis Costa Susana Vinga Ludwig Krippahl Marta B. Lopes |
| author_facet | Rafaela C. Cruz Pedro Reis Costa Susana Vinga Ludwig Krippahl Marta B. Lopes |
| author_sort | Rafaela C. Cruz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T05:19:59Z |
| format | Article |
| id | doaj.art-33a6227c5b5645908f38c4ef3ffa36cf |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T05:19:59Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-33a6227c5b5645908f38c4ef3ffa36cf2023-12-03T12:41:44ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-03-019328310.3390/jmse9030283A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish ContaminationRafaela C. Cruz0Pedro Reis Costa1Susana Vinga2Ludwig Krippahl3Marta B. Lopes4Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa (FCT NOVA), 2829-516 Caparica, PortugalIPMA—Instituto Português do Mar e da Atmosfera, 1495-165 Lisboa, PortugalINESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisboa, PortugalFaculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa (FCT NOVA), 2829-516 Caparica, PortugalFaculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa (FCT NOVA), 2829-516 Caparica, PortugalHarmful 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.https://www.mdpi.com/2077-1312/9/3/283marine biotoxinsshellfish productionharmful algal bloomstoxic phytoplanktonmultivariate time seriestime-series forecasting |
| spellingShingle | Rafaela C. Cruz Pedro Reis Costa Susana Vinga Ludwig Krippahl Marta B. Lopes A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination Journal of Marine Science and Engineering marine biotoxins shellfish production harmful algal blooms toxic phytoplankton multivariate time series time-series forecasting |
| title | 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_short | A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination |
| title_sort | review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination |
| topic | marine biotoxins shellfish production harmful algal blooms toxic phytoplankton multivariate time series time-series forecasting |
| url | https://www.mdpi.com/2077-1312/9/3/283 |
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