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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© 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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