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“Soft” geostatistical analysis of radioactive soil contamination

  • R. Parkin
  • E. Savelieva
  • M. Serre
Conference paper

Keywords

Geostatistical Analysis Soft Data Indicator Kriging Bayesian Maximum Entropy Spatial Correlation Structure 
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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References

  1. Christakos G (2000) Modern Spatiotemporal Geostatistics. Oxford University Press, New YorkGoogle Scholar
  2. Christakos G, Bogaert P, Serre ML (2002) Temporal GIS. Springer-Verlag, New YorkGoogle Scholar
  3. Goovaerts P (1997) Geostatistics for Natural Resources Evalution. Oxford University Press, New York OxfordGoogle Scholar
  4. Rivoirard J (1994) Introduction to Disjunctive Kriging and Non-linear Geostatistics. Clarendon Press, OxfordGoogle Scholar
  5. Saito H, Goovaerts P (2002) Accounting for measurement error in uncertainty modeling and decision making using indicator kriging and p-field simulation: Application to a dioxin contaminated site. Environmetrics, 13: 555–567CrossRefGoogle Scholar
  6. Savelieva E, Demyanov V, Kanevski M, Serre M, Christakos G (2003) BME Application for Uncertainty Assessment of the Chernobyl Fallouts. In: Book of Abstracts Pedometrics 2003, University of Reading, pp. 36–37Google Scholar
  7. Serre ML, Christakos G (1999) Modern geostatistics: Computational BME in the light of uncertain physical knowledge — The Equus Beds Study, Stochastic Environmental Research and Risk Assessment, 13: 1–26Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • R. Parkin
    • 1
  • E. Savelieva
    • 1
  • M. Serre
    • 2
  1. 1.Nuclear Safety Institute Russian Academy of SciencesMoscowRussia
  2. 2.Center for the Integrated Study of the EnvironmentSchool for Public Health, University of North CarolinaUSA

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