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Learning and Data Driver Methods for Short Term Meteo Forecast

  • Conference paper
Neural Nets (WIRN 2005, NAIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3931))

Abstract

In this paper an “intelligent systems” based on neural networks and a statistical non parametric method evaluates the future evolution of meteorological variables and the occurrence of particular phenomena such as rain and road ice. The meteorological variables forecast system is based on a recurrent feed forward multi layer perceptron which make the forecasts using data coming from synoptic electronic sensors. Road ice is forecasted using an analytical model.

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References

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Polytechnic of Turin, INFN of Turin, 10129, Turin, Italy

    Eros Pasero & Walter Moniaci

Authors
  1. Eros Pasero
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  2. Walter Moniaci
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Editor information

Editors and Affiliations

  1. Dipartimentimento di Scienze dell’Informazione, via Comelico 39/41, 20135, Milano, Italy

    Bruno Apolloni

  2. Dipartimento di Fisica “E.R. Caianiello”, Università degli Studi di Salerno, Via S. Allende, 84081, Baronissi (SA), Italy

    Maria Marinaro

  3. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Giuseppe Nicosia

  4. Department of Mathematics and Informatics, University of Salerno, Via Ponte Don Melillo, 84084, Fisciano (SA), Italy

    Roberto Tagliaferri

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© 2006 Springer-Verlag Berlin Heidelberg

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Pasero, E., Moniaci, W. (2006). Learning and Data Driver Methods for Short Term Meteo Forecast. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_16

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  • DOI: https://doi.org/10.1007/11731177_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33183-4

  • Online ISBN: 978-3-540-33184-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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