Focused small-scale fisheries as complex systems using deep learning models
| Autor: | Miguel Ángel Ojeda-Ruiz, Elvia Aida Marín-Monroy, Alfredo Flores-Irigollen, Ricardo Alberto Cavieses-Núñez, Mirtha lbañez-Lucero, Carlos Sánchez-Ortiz |
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| Rok vydání: | 2021 |
| Předmět: |
Sustainable development
finfish Food security Computer science Fishing Aquatic Science Oceanography Variety (cybernetics) Fishery Variable (computer science) artisanal fisheries Scale (social sciences) Predictive power Production (economics) complex systems artificial neural networks mathematical models |
| Zdroj: | Latin american journal of aquatic research v.49 n.2 2021 SciELO Chile CONICYT Chile instacron:CONICYT |
| ISSN: | 0718-560X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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