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Oliveira, M.M., AMC, JW, Vera Miguéis , Ferreira, N. B. & Miguel Gaspar (2019). Forecasting bivalve landings with multiple regression and data miningtechniques: The case of the Portuguese Artisanal Dredge Fleet. Marine Policy. 110-118
M. M. Oliveira et al., "Forecasting bivalve landings with multiple regression and data miningtechniques: The case of the Portuguese Artisanal Dredge Fleet", in Marine Policy, pp. 110-118, 2019
@article{oliveira2019_1732221716385, author = "Oliveira, M.M. and AMC and JW and Vera Miguéis and Ferreira, N. B. and Miguel Gaspar", title = "Forecasting bivalve landings with multiple regression and data miningtechniques: The case of the Portuguese Artisanal Dredge Fleet", journal = "Marine Policy", year = "2019", volume = "", number = "", pages = "110-118" }
TY - JOUR TI - Forecasting bivalve landings with multiple regression and data miningtechniques: The case of the Portuguese Artisanal Dredge Fleet T2 - Marine Policy AU - Oliveira, M.M. AU - AMC AU - JW AU - Vera Miguéis AU - Ferreira, N. B. AU - Miguel Gaspar PY - 2019 SP - 110-118 SN - 0308-597X AB - 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. ER -