ANN and Fuzzy c-Means applied to environmental pollution prediction

Cortina Januchs, María Guadalupe and Quintanilla Domínguez, Joel and Andina de la Fuente, Diego and Vega Corona, Antonio (2012). ANN and Fuzzy c-Means applied to environmental pollution prediction. 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: ANN and Fuzzy c-Means applied to environmental pollution prediction
Author/s:
  • Cortina Januchs, María Guadalupe
  • Quintanilla Domínguez, Joel
  • Andina de la Fuente, Diego
  • Vega Corona, Antonio
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

Salamanca, situated in center of Mexico is among the cities which suffer most from the air pollution in Mexico. The vehicular park and the industry, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Sulphur Dioxide (SO2). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables and air pollutant concentrations of SO2. Before the prediction, Fuzzy c-Means and K-means clustering algorithms have been implemented in order to find relationship among pollutant and meteorological variables. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of SO2 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours.

More information

Item ID: 19976
DC Identifier: http://oa.upm.es/19976/
OAI Identifier: oai:oa.upm.es:19976
Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6320985
Deposited by: Memoria Investigacion
Deposited on: 28 Sep 2013 07:27
Last Modified: 21 Apr 2016 22:03
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