Prediction of PM10 concentrations using Fuzzy c-Means and ANN

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

Descripción

Título: Prediction of PM10 concentrations using Fuzzy c-Means and ANN
Autor/es:
  • Cortina Januchs, María Guadalupe
  • Quintanilla Domínguez, Joel
  • Andina de la Fuente, Diego
  • Vega Corona, Antonio
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society
Fechas del Evento: 07/11/2011 - 10/11/2011
Lugar del Evento: Melbourne, Australia
Título del Libro: Proceedings of IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society
Fecha: 2011
Materias:
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 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

Más información

ID de Registro: 12240
Identificador DC: http://oa.upm.es/12240/
Identificador OAI: oai:oa.upm.es:12240
URL Oficial: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6119735
Depositado por: Memoria Investigacion
Depositado el: 28 Ago 2012 10:20
Ultima Modificación: 21 Abr 2016 11:27
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