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http://dx.doi.org/10.11108/kagis.2017.20.1.071

Quantitative Comparison of Univariate Kriging Algorithms for Radon Concentration Mapping  

KWAK, Geun-Ho (Department of Geoinformatic Engineering, Inha University)
KIM, Yong-Jae (Department of Natural Radiation Safety, Korea Institute of Nuclear Safety)
CHANG, Byung-Uck (Department of Natural Radiation Safety, Korea Institute of Nuclear Safety)
PARK, No-Wook (Department of Geoinformatic Engineering, Inha University)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.20, no.1, 2017 , pp. 71-84 More about this Journal
Abstract
Radon, which enters the interior environment from soil, rocks, and groundwater, is a radioactive gas that poses a serious risk to humans. Indoor radon concentrations are measured to investigate the risk of radon gas exposure and reliable radon concentration mapping is then performed for further analysis. In this study, we compared the predictive performance of various univariate kriging algorithms, including ordinary kriging and three nonlinear transform-based kriging algorithms (log-normal, multi-Gaussian, and indicator kriging), for mapping radon concentrations with an asymmetric distribution. To compare and analyze the predictive performance, we carried out jackknife-based validation and analyzed the errors according to the differences in the data intervals and sampling densities. From a case study in South Korea, the overall nonlinear transform-based kriging algorithms showed better predictive performance than ordinary kriging. Among the nonlinear transform-based kriging algorithms, log-normal kriging had the best performance, followed by multi-Gaussian kriging. Ordinary kriging was the best for predicting high values within the spatial pattern. The results from this study are expected to be useful in the selection of kriging algorithms for the spatial prediction of data with an asymmetric distribution.
Keywords
Indoor Radon Concentration; Spatial Prediction; Kriging; Asymmetric Distribution Data;
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Times Cited By KSCI : 6  (Citation Analysis)
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