Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Cortina Januchs, María Guadalupe and Barron Adame, Jose Miguel and Andina de la Fuente, Diego and Vega Corona, Antonio (2012). Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico. "Expert systems with applications", v. 39 (n. 1); pp. 107-116. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2011.05.083.

Description

Title: Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico
Author/s:
  • Cortina Januchs, María Guadalupe
  • Barron Adame, Jose Miguel
  • Andina de la Fuente, Diego
  • Vega Corona, Antonio
Item Type: Article
Título de Revista/Publicación: Expert systems with applications
Date: January 2012
ISSN: 0957-4174
Volume: 39
Subjects:
Freetext Keywords: Air pollution, meteorogical variables, artificial neural networks, self-organizing maps, clustering
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

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system 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: 15431
DC Identifier: http://oa.upm.es/15431/
OAI Identifier: oai:oa.upm.es:15431
DOI: 10.1016/j.eswa.2011.05.083
Official URL: http://www.sciencedirect.com/science/article/pii/S0957417411008670
Deposited by: Memoria Investigacion
Deposited on: 29 May 2013 19:26
Last Modified: 21 Apr 2016 15:29
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