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Oliveira, M. M., Camanho, A. S., Walden, J. B., Miguéis, V. L., Ferreira, N. B. & Gaspar, M. B. (2017). Forecasting bivalve landings with multiple regression and data mining techniques: the case of the Portuguese artisanal dredge fleet. Marine Policy. 84, 110-118

	. M. Oliveira et al.,  "Forecasting bivalve landings with multiple regression and data mining techniques: the case of the Portuguese artisanal dredge fleet", in Marine Policy, vol. 84, pp. 110-118, 2017

@article{oliveira2017_1734882092697,
	author = "Oliveira, M. M. and Camanho, A. S. and Walden, J. B. and Miguéis, V. L. and Ferreira, N. B. and Gaspar, M. B.",
	title = "Forecasting bivalve landings with multiple regression and data mining techniques: the case of the Portuguese artisanal dredge fleet",
	journal = "Marine Policy",
	year = "2017",
	volume = "84",
	number = "",
	doi = "10.1016/j.marpol.2017.07.013",
	pages = "110-118",
	url = "http://www.sciencedirect.com/science/article/pii/S0308597X17303159?via%3Dihub"
}

TY  - JOUR
TI  - Forecasting bivalve landings with multiple regression and data mining techniques: the case of the Portuguese artisanal dredge fleet
T2  - Marine Policy
VL  - 84
AU  - Oliveira, M. M.
AU  - Camanho, A. S.
AU  - Walden, J. B.
AU  - Miguéis, V. L.
AU  - Ferreira, N. B.
AU  - Gaspar, M. B.
PY  - 2017
SP  - 110-118
SN  - 0308-597X
DO  - 10.1016/j.marpol.2017.07.013
UR  - http://www.sciencedirect.com/science/article/pii/S0308597X17303159?via%3Dihub
AB  - This paper develops a decision support tool  that  can help  ?shery authorities to forecast bivalve landings for the dredge ?eet  accounting for  several contextual conditions. These  include weather conditions, phytotoxins epi- sodes,  stock-biomass indicators per  species  and  tourism levels.  Vessel characteristics and  ?shing e?ort 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  speci?ed for  di?erent 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  -