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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 5106045, 14 pages
https://doi.org/10.1155/2017/5106045
Research Article

Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

1University of Twente, Enschede, Netherlands
2Intelligent & Interactive Systems Lab (SI2 Lab), FICA, Universidad de Las Américas, Quito, Ecuador
3DEE, Nova University of Lisbon and CTS, UNINOVA, Monte de Caparica, Portugal

Correspondence should be addressed to Yves Rybarczyk; tp.lnu.tcf@kyzcrabyr.y

Received 24 February 2017; Revised 23 April 2017; Accepted 11 May 2017; Published 18 June 2017

Academic Editor: Lei Zhang

Copyright © 2017 Jan Kleine Deters et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [15 citations]

The following is the list of published articles that have cited the current article.

  • Yves Rybarczyk, and Rasa Zalakeviciute, “Regression Models to Predict Air Pollution from Affordable Data Collections,” Machine Learning - Advanced Techniques and Emerging Applications, 2018. View at Publisher · View at Google Scholar
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  • Rasa Zalakeviciute, Yves Rybarczyk, Jesús López-Villada, and Maria Valeria Diaz Suarez, “Quantifying decade-long effects of fuel and traffic regulations on urban ambient PM2.5 pollution in a mid-size South American city,” Atmospheric Pollution Research, vol. 9, no. 1, pp. 66–75, 2018. View at Publisher · View at Google Scholar
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