Abstract
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 PM 10. 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 PM 10 along a year. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM 10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).KeywordsWind SpeedRoot Mean Square ErrorWind DirectionMeteorological VariableAtmospheric EnvironmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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