Improving maritime traffic emission estimations on missing data with CRBMs-Reference-Cited by-同舟云学术

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Improving maritime traffic emission estimations on missing data with CRBMs

Published:2020-09 Issue: Volume:94 Page:103793
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Gutierrez-Torre Alberto,Berral Josep Ll.,Buchaca David,Guevara Marc,Soret Albert,Carrera David

Funder

European Research Council

Ministry of Economy, Industry and Competitiveness of Spain

Severo Ochoa Center of Excellence

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference36 articles.

1. Sistema CALIOPE, prediction of air quality;Anon,2019

2. Learning from time-changing data with adaptive windowing;Bifet,2007

3. Second IMO GHG Study 2009, Prevention of Air Pollution from Ships;Buhaug,2009

4. El arrastre: El arte de la pesca sostenible;Cepesca,2017

5. The economic value of the EU shipping industry. update;European Community Shipowners Associations (ECSA),2015

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2. Impact of trajectory simplification methods on modeling carbon dioxide emissions from ships;Ocean Engineering;2024-08

3. Prediction of harbour vessel emissions based on machine learning approach;Transportation Research Part D: Transport and Environment;2024-06

4. Analysis of the uncertainty of the AIS-based bottom-up approach for estimating ship emissions;Marine Pollution Bulletin;2024-02

5. A Novel Method for Imputing Missing Values in Ship Static Data Based on Generative Adversarial Networks;Journal of Marine Science and Engineering;2023-04-10

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