NDLI: Unsupervised method to classify PM pollutant concentrations

Content Provider	IEEE Xplore Digital Library
Author	Vega-Corona, A. Barron-Adame, J.M. Herrera-Delgado, J.A. Quintanilla-Dominguez, J. Cortina-Januchs, M.G. Andina, D.
Copyright Year	2012
Description	Author affiliation: University of Guanajuato, Salamanca, Mexico (Vega-Corona, A.; Barron-Adame, J.M.; Herrera-Delgado, J.A.) \|\| Technical University of Madrid, Spain (Quintanilla-Dominguez, J.; Cortina-Januchs, M.G.; Andina, D.)
Abstract	In this paper a method based mainly on Data Fusion and Artificial Neural Networks to classify one of the most important pollutants such as Particulate Matter less than 10 micrometer in diameter (PM) concentrations is proposed. The main objective is to classify in two pollution levels (Non-Contingency and Contingency) the pollutant concentration. Pollutant concentrations and meteorological variables have been considered in order to build a Representative Vector (RV) of pollution. RV is used to train an Artificial Neural Network in order to classify pollutant events determined by meteorological variables. In the experiments, real time series gathered from the Automatic Environmental Monitoring Network (AEMN) in Salamanca Guanajuato Mexico have been used. The method can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.
Starting Page	1
Ending Page	6
File Size	2025099
Page Count	6
File Format	PDF
ISBN	9781467344975
ISSN	21544824
e-ISBN	9781889334479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-24
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	TSI Press
Subject Keyword	Pollution Monitoring Chemicals Robot sensing systems Neurons Testing
Content Type	Text
Resource Type	Article

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