Estimation of fish catch potential using assimilation of synthetic measurements with an individual-based model
A large fraction of costs in wild fisheries are fuel related, and while much of the costs are related to gear used and stock targeted, search for fishing grounds also contributes to fuel costs. Lack of knowledge on the spatial abundance of stocks during the fishing season is a limiting factor for fi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2023.1171641/full |
| _version_ | 1797641176451383296 |
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| author | Cian Kelly Finn Are Michelsen Morten Omholt Alver |
| author_facet | Cian Kelly Finn Are Michelsen Morten Omholt Alver |
| author_sort | Cian Kelly |
| collection | DOAJ |
| description | A large fraction of costs in wild fisheries are fuel related, and while much of the costs are related to gear used and stock targeted, search for fishing grounds also contributes to fuel costs. Lack of knowledge on the spatial abundance of stocks during the fishing season is a limiting factor for fishing vessels when searching for suitable fishing grounds, and with better planning and routing, costs can be reduced. Strategic and tactical decision-making can be improved through operational decision support tools informed by real-time data and knowledge generated from research. In this article, we present a model-based estimation approach for predicting catch potential of ocean areas. An individual-based model of herring migrations is combined with an estimation approach known as Data Assimilation, which corrects model states using incoming data sources. The data used to correct the model are synthetic measurements generated from neural network output. Input to the neural network was vessel activity data of over 100 fishing vessels from 2015-2018, targeting mainly herring. The output is the predicted normalized density of herring in discrete grid cells. Model predictions are improved through assimilation of synthetic measurements with model states. Characterizing patterns from model output provides novel information on catch potential which can inform fishing activity. |
| first_indexed | 2024-03-11T13:41:49Z |
| format | Article |
| id | doaj.art-d51c5f5313af49d5a77f31c81c018427 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-03-11T13:41:49Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-d51c5f5313af49d5a77f31c81c0184272023-11-02T11:49:34ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452023-11-011010.3389/fmars.2023.11716411171641Estimation of fish catch potential using assimilation of synthetic measurements with an individual-based modelCian Kelly0Finn Are Michelsen1Morten Omholt Alver2Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayFisheries and New Biomarine Industry, SINTEF Ocean, Trondheim, NorwayDepartment of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayA large fraction of costs in wild fisheries are fuel related, and while much of the costs are related to gear used and stock targeted, search for fishing grounds also contributes to fuel costs. Lack of knowledge on the spatial abundance of stocks during the fishing season is a limiting factor for fishing vessels when searching for suitable fishing grounds, and with better planning and routing, costs can be reduced. Strategic and tactical decision-making can be improved through operational decision support tools informed by real-time data and knowledge generated from research. In this article, we present a model-based estimation approach for predicting catch potential of ocean areas. An individual-based model of herring migrations is combined with an estimation approach known as Data Assimilation, which corrects model states using incoming data sources. The data used to correct the model are synthetic measurements generated from neural network output. Input to the neural network was vessel activity data of over 100 fishing vessels from 2015-2018, targeting mainly herring. The output is the predicted normalized density of herring in discrete grid cells. Model predictions are improved through assimilation of synthetic measurements with model states. Characterizing patterns from model output provides novel information on catch potential which can inform fishing activity.https://www.frontiersin.org/articles/10.3389/fmars.2023.1171641/fullneural networksynthetic measurementsindividual-based modelensemble Kalman filterdata assimilationcatch potential |
| spellingShingle | Cian Kelly Finn Are Michelsen Morten Omholt Alver Estimation of fish catch potential using assimilation of synthetic measurements with an individual-based model Frontiers in Marine Science neural network synthetic measurements individual-based model ensemble Kalman filter data assimilation catch potential |
| title | Estimation of fish catch potential using assimilation of synthetic measurements with an individual-based model |
| title_full | Estimation of fish catch potential using assimilation of synthetic measurements with an individual-based model |
| title_fullStr | Estimation of fish catch potential using assimilation of synthetic measurements with an individual-based model |
| title_full_unstemmed | Estimation of fish catch potential using assimilation of synthetic measurements with an individual-based model |
| title_short | Estimation of fish catch potential using assimilation of synthetic measurements with an individual-based model |
| title_sort | estimation of fish catch potential using assimilation of synthetic measurements with an individual based model |
| topic | neural network synthetic measurements individual-based model ensemble Kalman filter data assimilation catch potential |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2023.1171641/full |
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