A feature extraction unsupervised neural network for an environmental data set
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Optimal Statistical Model for Forecasting Ozone
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
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In this paper two artificial neural networks are trained to determine Ozone and PM10 concentrations trying to model the environmental system. Then a method to partition the connection weights is used to calculate a relative importance index which returns the relative contribution of each chemical and meteorological input to the concentrations of Ozone and PM10. Moreover, an investigation of the variances of the input in the observation time contribute to understand which input mainly influence the output. Therefore a neural network trained only by the variables with higher values of relative importance index and low variability is used to improve the accuracy of the proposed model. The experimental results show that this approach could help to understand the environmental system.