Difference: Papers20100623123559 (1 vs. 2)

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META TOPICPARENT name="AI_GEOSTATSPapers"
 Title: Using Ordinary Kriging to Model Radioactive Contamination Data

Date:

Authors: Elena Savelieva

Link: http://publications.epress.monash.edu/doi/pdf/10.2104/ag050010

Abstract:

This paper deals with an application of ordinary kriging (OK) for spatial interpolation of data in a completely automatic ("one-click mapping") manner. The important set of kriging parameters (semivariogram model, search strategy, etc.) were tuned based on the prior characteristics of the phenomenon considered. The prior information provided as 10 sets of monitoring observations taken at different days was used to analyse and model the spatial correlation of the phenomenon. Furthermore, the prior information was expected to be consistent within a rather long time range and therefore assumed to reflect the structure of the contamination pattern at any given day. The approach applied here gave satisfactory results for both routine and emergency data sets. The benefits and drawbacks of the kriging model were well illustrated in the study. Ordinary kriging can be considered as a real candidate for the implementation in a decision support system.

Reference

Applied GIS
Volume 1, No. 2, August 2005
DOI: 10.2104/ag050010

-- TWikiAdminUser - 2010-06-16

Revision 12010-06-23 - TWikiAdminUser

 
META TOPICPARENT name="GeostatisticsPapers"
Title: Using Ordinary Kriging to Model Radioactive Contamination Data

Date:

Authors: Elena Savelieva

Link: http://publications.epress.monash.edu/doi/pdf/10.2104/ag050010

Abstract:

This paper deals with an application of ordinary kriging (OK) for spatial interpolation of data in a completely automatic ("one-click mapping") manner. The important set of kriging parameters (semivariogram model, search strategy, etc.) were tuned based on the prior characteristics of the phenomenon considered. The prior information provided as 10 sets of monitoring observations taken at different days was used to analyse and model the spatial correlation of the phenomenon. Furthermore, the prior information was expected to be consistent within a rather long time range and therefore assumed to reflect the structure of the contamination pattern at any given day. The approach applied here gave satisfactory results for both routine and emergency data sets. The benefits and drawbacks of the kriging model were well illustrated in the study. Ordinary kriging can be considered as a real candidate for the implementation in a decision support system.

Reference

Applied GIS
Volume 1, No. 2, August 2005
DOI: 10.2104/ag050010

-- TWikiAdminUser - 2010-06-16

 
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