Abstract
The work discusses methods dealing with “soft” input data, where local uncertainty is represented by a variance. Modifications of ordinary kriging and sequential direct stochastic simulations based on such data are applied to a real hydrogeological case study and a synthetic environmental contamination study. The modification performed on direct simulation approach does not require any data transformation assumptions. The method is compared with Bayesian Maximum Entropy (BME) based stochastic simulations, which provide an alternative way of integrating “soft” information.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barange M, Hampton I, Soule M (1996) Empirical determination of in situ target strengths of three loosely aggregated pelagic fish species. ICES J Marine Sci 53:225–232
Chiles J-P, Delfiner P (1999) Geostatistics. Modeling spatial uncertainty. Wiley, New York
Christakos G (2000) Modern spatiotemporal geostatistics. Oxford University Press, New York
Christakos G, Bogaert P, Serre M (2002) Temporal GIS. Springer, New York
Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York
Dubois G, Galmarini S (2005) Introduction to the spatial interpolation comparison (SIC) 2004 exercise and presentation of the data sets. Appl GIS 2:9-01–9-10
Nuzhny A, Savelieva E, Jastrebkov A (2007) Statistical analysis for extracting features on the groundwater level dynamics. In: Zhao P, Agterberg F, Cheng Q (eds) Proceedings of IAMG2007, Geomathematics and GIS analysis of resources, Environment and Hazards, Beijing, pp 723–726
Parkin R, Savelieva E, Serre M (2005) “Soft” geostatistical analysis of radioactive soil contamination. In: Renard P, Demougeot-Renard H, Froidevaux R (eds) Geostatistics for environmental applications. Springer, Berlin, pp 331–342
Savelieva E, Demyanov V, Kanevski M, Serre M, Christakos G (2005) BME_based uncertainty assessment of the chernobyl fallout. Geoderma 128:312–324
Savelieva E, Bizikov V, Goncharov S, Popov S, Mazzola S, Bonanno A, Patti B (2007) Stochastic simulations for assessment of uncertainty of spatial distribution and biomass of marine living resources. In: Proceedings of the 6th European Conference on Ecological Modeling (ECEM’07), Challenges for ecological modeling in a changing world: Global changes, sustainability and ecosystem management, pp 457–458. Trieste
Soares A (2001) Direct sequential simulation and cosimulation. Math Geol 33:911–926
Sokolov VI (2006) Stock assessment of red king crab (Paralithodes camtschaticus) in the Russian part of the Barents Sea basing on the trap survey data. In: Abstracts of the VIIth All-Russian Conference on Commercial Invertebrates. VNIRO Press, Murmansk, pp 129–132
Acknowledgements
The work was partly supported by Russian fund for fundamental researches (RFFI) 07-08-00257.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Savelyeva, E., Utkin, S., Kazakov, S., Demyanov, V. (2010). Modeling Spatial Uncertainty for Locally Uncertain Data. In: Atkinson, P., Lloyd, C. (eds) geoENV VII – Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2322-3_26
Download citation
DOI: https://doi.org/10.1007/978-90-481-2322-3_26
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-2321-6
Online ISBN: 978-90-481-2322-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)