Air pollution data classification by SOM Neural Network

Barron Adame, Jose Miguel and Ibarra Manzano, Óscar Gerardo and Vega Corona, Antonio and Cortina Januchs, María Guadalupe and Andina de la Fuente, Diego (2012). Air pollution data classification by SOM Neural Network. In: "World Automation Congress (WAC), 2012", 24/06/2012 - 28/06/2012, Puerto Vallarta, Mexico. ISBN 978-1-4673-4497-5. pp. 1-5.

Description

Title: Air pollution data classification by SOM Neural Network
Author/s:
  • Barron Adame, Jose Miguel
  • Ibarra Manzano, Óscar Gerardo
  • Vega Corona, Antonio
  • 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

Over the last ten years, Salamanca has been considered among the most polluted cities in México. This paper presents a Self-Organizing Maps (SOM) Neural Network application to classify pollution data and automatize the air pollution level determination for Sulphur Dioxide (SO2) in Salamanca. Meteorological parameters are well known to be important factors contributing to air quality estimation and prediction. In order to observe the behavior and clarify the influence of wind parameters on the SO2 concentrations a SOM Neural Network have been implemented along a year. The main advantages of the SOM 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. The results show a significative correlation between pollutant concentrations and some environmental variables.

More information

Item ID: 19973
DC Identifier: http://oa.upm.es/19973/
OAI Identifier: oai:oa.upm.es:19973
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6320993
Deposited by: Memoria Investigacion
Deposited on: 25 Sep 2013 18:26
Last Modified: 21 Apr 2016 22:02
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