Forecasting bivalve landings with multiple regression and data mining techniques: The case of the Portuguese Artisanal Dredge Fleet |
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| Institution: | 1. INESC-TEC, Portugal;2. Faculdade de Ciências da Economia e da Empresa, Universidade Lusíada de Lisboa, Portugal;3. Faculdade de Engenharia da Universidade do Porto, Portugal;4. NOAA Fisheries, Northeast Fisheries Science Center, USA;5. IBS-ISCTE IUL, Lisboa, Portugal;6. Instituto Português do Mar e da Atmosfera I.P./IPMA, Portugal;7. Centro de Ciências do Mar, Universidade do Algarve, Portugal;1. Department of Fisheries and Aquatic Resources, New Secretariat, Maligawatte, Colombo 10, Sri Lanka;2. Faculty of Agriculture, Ubon Ratchathani University, Ubon Ratchathani, 34190, Thailand;3. Department of Zoology and Environmental Management, University of Kelaniya, Kelaniya 11600, Sri Lanka;1. National Research Council (CNR), Institute for Coastal Marine Environment (IAMC), Spianata S. Raineri, 86, 98122, Messina, Italy;2. National Research Council (CNR), Institute for Coastal Marine Environment (IAMC), Via Roma 3, 74121, Taranto, Italy;3. CoNISMa, University of Trieste, Via Valerio 28/1, 34127, Trieste, Italy;4. CNPq, UFRJ, Institute of Biology, Av. Prof. R. Rocco 211, 21.941-617, Rio de Janeiro, Brazil;5. National Research Council (CNR), Institute for Coastal Marine Environment (IAMC), Via Del Mare 3, 91021, Capo Granitola, Trapani, Italy;1. Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand;2. Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s/n, Ed. FC4, 4169-007, Porto, Portugal;3. IPMA – Portuguese Institute for the Ocean and Atmosphere, Avenida Alfredo Magalhães Ramalho 6, 1495-165, Algés, Portugal;4. CCMAR – Centre for Marine Sciences, University of Algarve, Campus de Gambelas, 8005-139, Faro, Portugal;1. Center for Marine Research, Rudjer Boskovic Institute, Rovinj, Croatia;2. University of Dubrovnik, Institute for Marine and Coastal Research, Dubrovnik, Croatia |
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| Abstract: | This paper develops a decision support tool that can help fishery authorities to forecast bivalve landings for the dredge fleet accounting for several contextual conditions. These include weather conditions, phytotoxins episodes, stock-biomass indicators per species and tourism levels. Vessel characteristics and fishing effort are also taken into account for the estimation of landings. The relationship between these factors and monthly quantities landed per vessel is explored using multiple linear regression models and data mining techniques (random forests, support vector machines and neural networks). The models are specified for different regions in the Portugal mainland (Northwest, Southwest and South) using six years of data 2010–2015). Results showed that the impact of the contextual factors varies between regions and also depends on the vessels target species. The data mining techniques, namely the random forests, proved to be a robust decision support tool in this context, outperforming the predictive performance of the most popular technique used in this context, i.e. linear regression. |
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| Keywords: | Data mining Random forests Multiple regression Forecasting Small scale fisheries Bivalve fisheries |
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