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Descripción
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| Air pollution is one of the most important environmental problems. The prediction of air pollutant concentrations would allow taking preventive measures such as reducing the pollutant emission to the atmosphere. This paper presents a pollution alarm system used to predict the air pollution concentrations in Salamanca, Mexico. The work focuses on the daily maximum concentration of PM10. A Feed Forward Neural Network has been used to make the prediction. 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 along a year. 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). | |
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Internacional
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Si |
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Nombre congreso
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IWANN 2009 |
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Tipo de participación
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960 |
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Lugar del congreso
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Salamanca, España |
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Revisores
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Si |
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ISBN o ISSN
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978-3-642-02477-1 |
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DOI
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10.1007/978-3-642-02478-8_167 |
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Fecha inicio congreso
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10/06/2009 |
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Fecha fin congreso
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12/06/2009 |
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Desde la página
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1336 |
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Hasta la página
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1343 |
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Título de las actas
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Bio-Inspired Systems : Computational and Ambient Intelligence (IWANN 039;09). LCNS 5517 |