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Descripción
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| Air pollution is one of the most important environmental problems. Sulphur Dioxide (SO2) and Suspended Particles are considered the most important atmospheric pollutants. The prevision of industrial SO2 air pollutant concentrations would allow us to take preventive measures such as reducing the pollutant emission to the atmosphere. In This work we apply Feed Forward Artificial Neural Network to predict the air pollution concentrations in Salamanca, Mexico. The work focuses on the daily maximum concentration of SO2. 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 concentrations of SO2 along a year. Results of the experiments with the proposed system show the importance of the meteorological variable set on the prediction of SO2 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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Industrial Informatics, 2009. INDIN 2009. 7th IEEE International Conference on |
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Tipo de participación
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960 |
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Lugar del congreso
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Cardiff, UK |
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Revisores
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Si |
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ISBN o ISSN
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1935-4576 |
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DOI
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10.1109/INDIN.2009.5195856 |
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Fecha inicio congreso
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23/06/2009 |
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Fecha fin congreso
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26/06/2009 |
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Desde la página
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510 |
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Hasta la página
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515 |
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Título de las actas
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Proc. of Industrial Informatics, 2009. INDIN 2009. 7th IEEE International Conference on |