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Mapping of soil contamination by using artificial neural networks and multivariate geostatistics

  • Part VII: Prediction, Forecasting, and Monitoring
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

The work deals with the development and use of mixed models (artificial neural networks-ANN and modern geostatistical models) for the analysis of spatially distributed environmental data. When multivariate data have complex non-linear trends or high variability at different scales in the region of study it is proposed to use ANN to model non-linear large scale structures (trends) and then to apply multivariate geostatistics (co-kriging models) to the residuals. The proposed model is used for the spatial prediction of soil contamination by Chernobyl radionuclides.

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References

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Author information

Authors and Affiliations

  1. Institute of Nuclear Safety (IBRAE), B.Tulskaya 52, 113191, Moscow, Russia

    M. Kanevski & V. Demyanov

  2. University of Lausanne, Russia

    M. Maignan

Authors
  1. M. Kanevski
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  2. V. Demyanov
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  3. M. Maignan
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Kanevski, M., Demyanov, V., Maignan, M. (1997). Mapping of soil contamination by using artificial neural networks and multivariate geostatistics. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020304

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  • DOI: https://doi.org/10.1007/BFb0020304

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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