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

  • Giuseppe Acciani
  • Ernesto Chiarantoni
  • Girolamo Fornarelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

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.

Keywords

Neural Network Hide Layer Ozone Concentration Meteorological Variable Connection Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Giuseppe Acciani
    • 1
  • Ernesto Chiarantoni
    • 1
  • Girolamo Fornarelli
    • 1
  1. 1.Politecnico di Bari, Dipartimento di Elettronica ed ElettrotecnicaBariItaly

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