Unsupervised method to classify PM10 pollutant concentrations

Vega Corona, Antonio and Barron Adame, Jose Miguel and Herrera Delgado, J. A. and Quintanilla Domínguez, Joel and Cortina Januchs, María Guadalupe and Andina de la Fuente, Diego (2012). Unsupervised method to classify PM10 pollutant concentrations. In: "World Automation Congress (WAC), 2012", 24/06/2012 - 28/06/2012, Puerto Vallarta, Mexico. ISBN 978-1-4673-4497-5. pp. 1-6.

Description

Title: Unsupervised method to classify PM10 pollutant concentrations
Author/s:
  • Vega Corona, Antonio
  • Barron Adame, Jose Miguel
  • Herrera Delgado, J. A.
  • Quintanilla Domínguez, Joel
  • Cortina Januchs, María Guadalupe
  • Andina de la Fuente, Diego
Item Type: Presentation at Congress or Conference (Article)
Event Title: World Automation Congress (WAC), 2012
Event Dates: 24/06/2012 - 28/06/2012
Event Location: Puerto Vallarta, Mexico
Title of Book: World Automation Congress (WAC), 2012
Date: June 2012
ISBN: 978-1-4673-4497-5
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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 (PM10) 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.

More information

Item ID: 19971
DC Identifier: http://oa.upm.es/19971/
OAI Identifier: oai:oa.upm.es:19971
Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6320991
Deposited by: Memoria Investigacion
Deposited on: 25 Sep 2013 17:12
Last Modified: 21 Apr 2016 22:01
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