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dc.contributorConsejo Nacional de Ciencia y Tecnología (CONACYT)en-US
dc.creatorCavieses-Núñez, Ricardo
dc.creatorOjeda-Ruiz, Miguel A.
dc.creatorFlores-Irigollen, Alfredo
dc.creatorMarín-Monroy, Elvia
dc.creatorAlbañez-Lucero, Mirtha
dc.creatorSánchez-Ortíz, Carlos
dc.date2021-05-03
dc.identifierhttp://lajar.ucv.cl/index.php/rlajar/article/view/vol49-issue2-fulltext-2622
dc.identifier10.3856/vol49-issue2-fulltext-2622
dc.descriptionSmall-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.en-US
dc.formatapplication/pdf
dc.languageeng
dc.publisherPontificia Universidad Católica de Valparaísoen-US
dc.relationhttp://lajar.ucv.cl/index.php/rlajar/article/view/vol49-issue2-fulltext-2622/1401
dc.rightsCopyright (c) 2021 Latin American Journal of Aquatic Researchen-US
dc.sourceLatin American Journal of Aquatic Research; Vol 49, No 2 (2021); 342-353en-US
dc.sourcePlataforma para envío de artículos - Latin American Journal of Aquatic Research; Vol 49, No 2 (2021); 342-353es-ES
dc.source0718-560X
dc.source0718-560X
dc.subjectfinfish; artisanal fisheries; artificial neural networks; complex systems; mathematical modelsen-US
dc.titleFocused small-scale fisheries as complex systems using deep learning modelsen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeen-US
dc.typees-ES


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