Focused small-scale fisheries as complex systems using deep learning models

ABSTRACT Small-scale fishing (SSF) is a relevant economic activity worldwide, so sustainable development will be essential to assure its contributions to food security, poverty alleviation, and healthy ecosystems. However, the wide diversity of fisheries, their complexity, and the lack of informatio...

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Autores principales:	Cavieses-Núñez,Ricardo, Ojeda-Ruiz,Miguel A., Flores-Irigollen,Alfredo, Marín-Monroy,Elvia, lbañez-Lucero,Mirtha, Sánchez-Ortíz,Carlos
Lenguaje:	English
Publicado:	Pontificia Universidad Católica de Valparaíso. Facultad de Recursos Naturales. Escuela de Ciencias del Mar 2021
Materias:	finfish artisanal fisheries artificial neural networks complex systems mathematical models
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-560X2021000200342
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id	oai:scielo:S0718-560X2021000200342
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spelling	oai:scielo:S0718-560X20210002003422021-06-10Focused small-scale fisheries as complex systems using deep learning modelsCavieses-Núñez,RicardoOjeda-Ruiz,Miguel A.Flores-Irigollen,AlfredoMarín-Monroy,Elvialbañez-Lucero,MirthaSánchez-Ortíz,Carlos finfish artisanal fisheries artificial neural networks complex systems mathematical models ABSTRACT Small-scale fishing (SSF) is a relevant economic activity worldwide, so sustainable development will be essential to assure its contributions to food security, poverty alleviation, and healthy ecosystems. However, the wide diversity of fisheries, their complexity, and the lack of information limit the ability to propose/evaluate management measures and plans and their effects on communities and other productive activities. The state of Baja California Sur, Mexico, our study case, ranks as the third place in national fisheries production, possesses SSF fleets, has a wide variety of fisheries that share fishing areas, fishing seasons, and operating units. In this work, assuming SSF as a complex system were proposed deep learning models (DLM) to forecast the catch volumes, evaluate each input variable's importance, and find interactions. Environmental variables and catch fisheries were tested in the DLM to estimate their predictive power. Different DLM structures and parameters to find the optimal model was used. The variables that presented higher predictive power are the environmental variables with R = 0.90. Moreover, when used in combination with the catches from other areas, the performance of R = 0.95 is obtained. Using only the catches, the model has an R = 0.81. This model allows the use of variables that indirectly affect the system and demonstrates a useful tool to assess a complex system's state in the face of disturbances in its variables.info:eu-repo/semantics/openAccessPontificia Universidad Católica de Valparaíso. Facultad de Recursos Naturales. Escuela de Ciencias del MarLatin american journal of aquatic research v.49 n.2 20212021-05-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-560X2021000200342en10.3856/vol49-issue2-fulltext-2622
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	finfish artisanal fisheries artificial neural networks complex systems mathematical models
spellingShingle	finfish artisanal fisheries artificial neural networks complex systems mathematical models Cavieses-Núñez,Ricardo Ojeda-Ruiz,Miguel A. Flores-Irigollen,Alfredo Marín-Monroy,Elvia lbañez-Lucero,Mirtha Sánchez-Ortíz,Carlos Focused small-scale fisheries as complex systems using deep learning models
description	ABSTRACT Small-scale fishing (SSF) is a relevant economic activity worldwide, so sustainable development will be essential to assure its contributions to food security, poverty alleviation, and healthy ecosystems. However, the wide diversity of fisheries, their complexity, and the lack of information limit the ability to propose/evaluate management measures and plans and their effects on communities and other productive activities. The state of Baja California Sur, Mexico, our study case, ranks as the third place in national fisheries production, possesses SSF fleets, has a wide variety of fisheries that share fishing areas, fishing seasons, and operating units. In this work, assuming SSF as a complex system were proposed deep learning models (DLM) to forecast the catch volumes, evaluate each input variable's importance, and find interactions. Environmental variables and catch fisheries were tested in the DLM to estimate their predictive power. Different DLM structures and parameters to find the optimal model was used. The variables that presented higher predictive power are the environmental variables with R = 0.90. Moreover, when used in combination with the catches from other areas, the performance of R = 0.95 is obtained. Using only the catches, the model has an R = 0.81. This model allows the use of variables that indirectly affect the system and demonstrates a useful tool to assess a complex system's state in the face of disturbances in its variables.
author	Cavieses-Núñez,Ricardo Ojeda-Ruiz,Miguel A. Flores-Irigollen,Alfredo Marín-Monroy,Elvia lbañez-Lucero,Mirtha Sánchez-Ortíz,Carlos
author_facet	Cavieses-Núñez,Ricardo Ojeda-Ruiz,Miguel A. Flores-Irigollen,Alfredo Marín-Monroy,Elvia lbañez-Lucero,Mirtha Sánchez-Ortíz,Carlos
author_sort	Cavieses-Núñez,Ricardo
title	Focused small-scale fisheries as complex systems using deep learning models
title_short	Focused small-scale fisheries as complex systems using deep learning models
title_full	Focused small-scale fisheries as complex systems using deep learning models
title_fullStr	Focused small-scale fisheries as complex systems using deep learning models
title_full_unstemmed	Focused small-scale fisheries as complex systems using deep learning models
title_sort	focused small-scale fisheries as complex systems using deep learning models
publisher	Pontificia Universidad Católica de Valparaíso. Facultad de Recursos Naturales. Escuela de Ciencias del Mar
publishDate	2021
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-560X2021000200342
work_keys_str_mv	AT caviesesnunezricardo focusedsmallscalefisheriesascomplexsystemsusingdeeplearningmodels AT ojedaruizmiguela focusedsmallscalefisheriesascomplexsystemsusingdeeplearningmodels AT floresirigollenalfredo focusedsmallscalefisheriesascomplexsystemsusingdeeplearningmodels AT marinmonroyelvia focusedsmallscalefisheriesascomplexsystemsusingdeeplearningmodels AT lbanezluceromirtha focusedsmallscalefisheriesascomplexsystemsusingdeeplearningmodels AT sanchezortizcarlos focusedsmallscalefisheriesascomplexsystemsusingdeeplearningmodels
_version_	1714205249713995776

Focused small-scale fisheries as complex systems using deep learning models

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