Artigo em revista científica
Forecasting bivalve landings with multiple regression and data miningtechniques: The case of the Portuguese Artisanal Dredge Fleet
Manuela Maria Oliveira (Oliveira, M.M.); Ana Maria Camanho (AMC); John Walden (JW); Vera Miguéis (Vera Miguéis ); Nuno Ferreira (Ferreira, N. B.); Miguel B. Gaspar (Miguel Gaspar);
Título Revista
Marine Policy
Ano (publicação definitiva)
2019
Língua
--
País
Reino Unido
Mais Informação
--
Web of Science®

Esta publicação não está indexada na Web of Science®

Scopus

Esta publicação não está indexada na Scopus

Google Scholar

Esta publicação não está indexada no Google Scholar

Abstract/Resumo
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.
Agradecimentos/Acknowledgements
--
Palavras-chave
Data mining,Random forests,Multiple regression,Forecasting,Small scalefisheries,Bivalve fisheries