Title of article :
Air quality prediction using optimal neural networks with stochastic variables
Author/Authors :
Russo، نويسنده , , Ana and Raischel، نويسنده , , Frank and Lind، نويسنده , , Pedro G.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2013
Abstract :
We apply recent methods in stochastic data analysis for discovering a set of few stochastic variables that represent the relevant information on a multivariate stochastic system, used as input for artificial neural network models for air quality forecast. We show that using these derived variables as input variables for training the neural networks it is possible to significantly reduce the amount of input variables necessary for the neural network model, without considerably changing the predictive power of the model. The reduced set of variables including these derived variables is therefore proposed as an optimal variable set for training neural network models in forecasting geophysical and weather properties. Finally, we briefly discuss other possible applications of such optimized neural network models.
Keywords :
Pollutants , NEURAL NETWORKS , Stochastic systems , Environmental research
Journal title :
Atmospheric Environment
Journal title :
Atmospheric Environment