Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning...

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Published in:Ecological Informatics
Main Authors: Fernandes, JA, Irigoien, X, Lozano, JA, Inza, I, Goikoetxea, N, Pérez, A
Format: Article in Journal/Newspaper
Language:unknown
Published: 2015
Subjects:
Biology
Computer Science
Data and Information
Ecology and Environment
Fisheries
Management
Marine Sciences
Meteorology and Climatology
Oceanography
Planning
Policies
Technology
Hake
North East Atlantic
Online Access:http://plymsea.ac.uk/id/eprint/6863/
https://doi.org/10.1016/j.ecoinf.2014.11.004
id ftplymouthml:oai:plymsea.ac.uk:6863
record_format openpolar
spelling ftplymouthml:oai:plymsea.ac.uk:6863 2023-05-15T17:38:19+02:00 Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species Fernandes, JA Irigoien, X Lozano, JA Inza, I Goikoetxea, N Pérez, A 2015-01-03 http://plymsea.ac.uk/id/eprint/6863/ https://doi.org/10.1016/j.ecoinf.2014.11.004 unknown Fernandes, JA; Irigoien, X; Lozano, JA; Inza, I; Goikoetxea, N; Pérez, A. 2015 Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species. Ecological Informatics, 25. 35-42. https://doi.org/10.1016/j.ecoinf.2014.11.004 <https://doi.org/10.1016/j.ecoinf.2014.11.004> Biology Computer Science Data and Information Ecology and Environment Fisheries Management Marine Sciences Meteorology and Climatology Oceanography Planning Policies Technology Publication - Article PeerReviewed 2015 ftplymouthml https://doi.org/10.1016/j.ecoinf.2014.11.004 2022-09-13T05:48:45Z The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts. Article in Journal/Newspaper North East Atlantic Plymouth Marine Science Electronic Archive (PlyMSEA - Plymouth Marine Laboratory, PML) Hake ENVELOPE(15.612,15.612,66.797,66.797) Ecological Informatics 25 35 42
institution Open Polar
collection Plymouth Marine Science Electronic Archive (PlyMSEA - Plymouth Marine Laboratory, PML)
op_collection_id ftplymouthml
language unknown
topic Biology
Computer Science
Data and Information
Ecology and Environment
Fisheries
Management
Marine Sciences
Meteorology and Climatology
Oceanography
Planning
Policies
Technology
spellingShingle Biology
Computer Science
Data and Information
Ecology and Environment
Fisheries
Management
Marine Sciences
Meteorology and Climatology
Oceanography
Planning
Policies
Technology
Fernandes, JA
Irigoien, X
Lozano, JA
Inza, I
Goikoetxea, N
Pérez, A
Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species
topic_facet Biology
Computer Science
Data and Information
Ecology and Environment
Fisheries
Management
Marine Sciences
Meteorology and Climatology
Oceanography
Planning
Policies
Technology
description The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.
format Article in Journal/Newspaper
author Fernandes, JA
Irigoien, X
Lozano, JA
Inza, I
Goikoetxea, N
Pérez, A
author_facet Fernandes, JA
Irigoien, X
Lozano, JA
Inza, I
Goikoetxea, N
Pérez, A
author_sort Fernandes, JA
title Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species
title_short Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species
title_full Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species
title_fullStr Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species
title_full_unstemmed Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species
title_sort evaluating machine-learning techniques for recruitment forecasting of seven north east atlantic fish species
publishDate 2015
url http://plymsea.ac.uk/id/eprint/6863/
https://doi.org/10.1016/j.ecoinf.2014.11.004
long_lat ENVELOPE(15.612,15.612,66.797,66.797)
geographic Hake
geographic_facet Hake
genre North East Atlantic
genre_facet North East Atlantic
op_relation Fernandes, JA; Irigoien, X; Lozano, JA; Inza, I; Goikoetxea, N; Pérez, A. 2015 Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species. Ecological Informatics, 25. 35-42. https://doi.org/10.1016/j.ecoinf.2014.11.004 <https://doi.org/10.1016/j.ecoinf.2014.11.004>
op_doi https://doi.org/10.1016/j.ecoinf.2014.11.004
container_title Ecological Informatics
container_volume 25
container_start_page 35
op_container_end_page 42
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