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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
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
Exportar Referência (IEEE) 
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
Exportar BibTeX 
@article{oliveira2019_1734879950720,
	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"
}
Exportar RIS 
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  -