کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5117974 1485495 2017 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Forecasting bivalve landings with multiple regression and data mining techniques: The case of the Portuguese Artisanal Dredge Fleet
ترجمه فارسی عنوان
پیش بینی فرود دوچرخه سواری با رگرسیون چندگانه و تکنیک های داده کاوی: مورد ناوگان دلفریشی کاردانی پرتغالی
کلمات کلیدی
داده کاوی، جنگل های تصادفی، رگرسیون چندگانه، پیش بینی، ماهیگیری کوچک دوچرخه سواری،
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی دریا (اقیانوس)
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Marine Policy - Volume 84, October 2017, Pages 110-118
نویسندگان
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