Forecasting bivalve landings with multiple regression and data mining techniques: The case of the Portuguese Artisanal Dredge Fleet

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Oliveira, MM (Author) Other; Ana Maria Cunha Ribeiro dos Santos Ponces Camanho (Author) FEUP; Walden, JB (Author) Other; Vera L. Miguéis (Author) FEUP; Ferreira, NB (Author) Other; Gaspar, MB (Author) Other

Journal

Title: Marine Policy

Vol. 84

Pages: 110-118

ISSN: 0308-597X

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00N-5QA

DOI: 10.1016/j.marpol.2017.07.013

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9