Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Cortina Januchs, María Guadalupe and Quintanilla Domínguez, Joel and Andina de la Fuente, Diego and Vega Corona, Antonio (2011). Prediction of PM10 concentrations using Fuzzy c-Means and ANN. In: "IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society", 07/11/2011 - 10/11/2011, Melbourne, Australia.

Description

Title: Prediction of PM10 concentrations using Fuzzy c-Means and ANN
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: IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society
Event Dates: 07/11/2011 - 10/11/2011
Event Location: Melbourne, Australia
Title of Book: Proceedings of IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society
Date: 2011
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 has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). 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 (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 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 shown 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: 12240
DC Identifier: http://oa.upm.es/12240/
OAI Identifier: oai:oa.upm.es:12240
Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6119735
Deposited by: Memoria Investigacion
Deposited on: 28 Aug 2012 10:20
Last Modified: 21 Apr 2016 11:27
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