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.

A Neural Network Approach to Study O3 and PM10 Concentration in Environmental Pollution

Giuseppe Acciani;Ernesto Chiarantoni;Girolamo Fornarelli
2006-01-01

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.
Artificial Neural Networks, ICANN 2006 : 16th International Conference, Athens, Greece, September 10-14, 2006. Proceedings, Part II
978-3-540-38871-5
Springer
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/20587
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