Air quality prediction using optimal neural networks with stochastic variables

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 networks 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 optimal variable set for training neural networks models in forecasting geophysical and weather properties. Finally, we briefly discuss other possible applications of such optimized neural network models.

References in corpus (2)

• On the proper reconstruction of complex dynamical systems spoilt by strong measurement noise
• Uncovering wind turbine properties through two-dimensional stochastic modeling of wind dynamics

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