Pollutant concentrations and Meteorological data classification by Neural Networks

Vega Corona, Antonio, Barron Adame, Jose Miguel, Ibarra Manzano, Óscar Gerardo, Cortina Januchs, María Guadalupe, Quintanilla Domínguez, Joel and Andina de la Fuente, Diego ORCID: https://orcid.org/0000-0001-7036-2646 (2012). Pollutant concentrations and Meteorological data classification by Neural Networks. In: "World Automation Congress (WAC), 2012", 24/06/2012 - 28/06/2012, Puerto Vallarta (México). pp. 1-6.

Description

Title: Pollutant concentrations and Meteorological data classification by Neural Networks
Author/s:
  • Vega Corona, Antonio
  • Barron Adame, Jose Miguel
  • Ibarra Manzano, Óscar Gerardo
  • Cortina Januchs, María Guadalupe
  • Quintanilla Domínguez, Joel
  • Andina de la Fuente, Diego https://orcid.org/0000-0001-7036-2646
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 (México)
Title of Book: World Automation Congress (WAC), 2012
Date: 2012
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

This paper present an environmental contingency forecasting tool based on Neural Networks (NN). Forecasting tool analyzes every hour and daily Sulphur Dioxide (SO2) concentrations and Meteorological data time series. Pollutant concentrations and meteorological variables are self-organized applying a Self-organizing Map (SOM) NN in different classes. Classes are used in training phase of a General Regression Neural Network (GRNN) classifier to provide an air quality forecast. In this case a time series set obtained from Environmental Monitoring Network (EMN) of the city of Salamanca, Guanajuato, México is used. Results verify the potential of this method versus other statistical classification methods and also variables correlation is solved.

More information

Item ID: 19970
DC Identifier: https://oa.upm.es/19970/
OAI Identifier: oai:oa.upm.es:19970
Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...
Deposited by: Memoria Investigacion
Deposited on: 25 Sep 2013 16:50
Last Modified: 21 Apr 2016 22:01
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