Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Acciani, G., Chiarantoni, E., Fornarelli, G. (2006). A Neural Network Approach to Study O3 and PM10 Concentration in Environmental Pollution. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_95
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DOI: https://doi.org/10.1007/11840930_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
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