Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Cortina Januchs, María Guadalupe; Barron Adame, Jose Miguel; Andina de la Fuente, Diego y 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.

Descripción

Título: Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico
Autor/es:
  • Cortina Januchs, María Guadalupe
  • Barron Adame, Jose Miguel
  • Andina de la Fuente, Diego
  • Vega Corona, Antonio
Tipo de Documento: Artículo
Título de Revista/Publicación: Expert systems with applications
Fecha: Enero 2012
Volumen: 39
Materias:
Palabras Clave Informales: Air pollution, meteorogical variables, artificial neural networks, self-organizing maps, clustering
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 15431
Identificador DC: http://oa.upm.es/15431/
Identificador OAI: oai:oa.upm.es:15431
Depositado por: Memoria Investigacion
Depositado el: 29 May 2013 19:26
Ultima Modificación: 21 Abr 2016 15:29
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