Air Pollutant Level Estimation Applying a Self-organizing Neural Network

This paper presents a novel Neural Network application in order to estimate Air Pollutant Levels. The application considers both Pollutant concentrations and Meteorological variables. In order to compute the Air Pollutant Level the method considers three important stages. In first stage, A process to validate data information and built a threedimensional Information Feature Vector with Pollutant concentrations and both wind speed and wind direction meteorological variables is developed. The information Feature Vector is orderly like a time series to estimate the Air Pollutant Level. In second stage, considering the behavior space knowledge a priori about pollutant and meteorological variables distribution a threedimensional Representative Vector is built in order to reduces the computational cost in Neural Network training process. In last stage, a Neural Network is designed and trained with the Threedimensional Representative Vector, then using the Threedimensional Information Feature Vector the Air Pollutant Level is estimated. This paper considers a real time series from an Automatic Environmental Monitoring Network from Salamanca, Guanajuato, Mexico, and therefore in this proposal a real Air Pollutant Level is also estimated.