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Building on crossvalidation for increasing the quality of geostatistical modeling

Stochastic Environmental Research and Risk Assessment

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DOI: 10.1007/s00477-011-0496-2

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Abstract

The random function is a mathematical model commonly used in the assessment of uncertainty associated with a spatially correlated attribute that has been partially sampled. There are multiple algorithms for modeling such random functions, all sharing the requirement of specifying various parameters that have critical influence on the results. The importance of finding ways to compare the methods and setting parameters to obtain results that better model uncertainty has increased as these algorithms have grown in number and complexity. Crossvalidation has been used in spatial statistics, mostly in kriging, for the analysis of mean square errors. An appeal of this approach is its ability to work with the same empirical sample available for running the algorithms. This paper goes beyond checking estimates by formulating a function sensitive to conditional bias. Under ideal conditions, such function turns into a straight line, which can be used as a reference for preparing measures of performance. Applied to kriging, deviations from the ideal line provide sensitivity to the semivariogram lacking in crossvalidation of kriging errors and are more sensitive to conditional bias than analyses of errors. In terms of stochastic simulation, in addition to finding better parameters, the deviations allow comparison of the realizations resulting from the applications of different methods. Examples show improvements of about 30% in the deviations and approximately 10% in the square root of mean square errors between reasonable starting modelling and the solutions according to the new criteria. ?? 2011 US Government.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Building on crossvalidation for increasing the quality of geostatistical modeling
Series title:
Stochastic Environmental Research and Risk Assessment
DOI:
10.1007/s00477-011-0496-2
Volume
26
Issue:
1
Year Published:
2012
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Stochastic Environmental Research and Risk Assessment
First page:
73
Last page:
82
Number of Pages:
10