• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.321-338
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• 2009

The objective of this paper is to compare spatial prediction capabilities of univariate kriging algorithms for generating GIS-based thematic maps from ground survey data with asymmetric distributions. Four univariate kriging algorithms including traditional ordinary kriging, three non-linear transform-based kriging algorithms such as log-normal kriging, multi-Gaussian kriging and indicator kriging are applied for spatial interpolation of geochemical As and Pb elements. Cross validation based on a leave-one-out approach is applied and then prediction errors are computed. The impact of the sampling density of the ground survey data on the prediction errors are also investigated. Through the case study, indicator kriging showed the smallest prediction errors and superior prediction capabilities of very low and very high values. Other non-linear transform based kriging algorithms yielded better prediction capabilities than traditional ordinary kriging. Log-normal kriging which has been widely applied, however, produced biased estimation results (overall, overestimation). It is expected that such quantitative comparison results would be effectively used for the selection of an optimal kriging algorithm for spatial interpolation of ground survey data with asymmetric distributions.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.225-228
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• 2007

This paper presents a geostatistical methodology to model local uncertainty in spatial estimation of sediment grain size with high-resolution remote sensing imagery. Within a multi-Gaussian framework, the IKONOS imagery is used as local means both to estimate the grain size values and to model local uncertainty at unsample locations. A conditional cumulative distribution function (ccdf) at any locations is defined by mean and variance values which can be estimated by multi-Gaussian kriging with local means. Two ccdf statistics including condition variance and interquartile range are used here as measures of local uncertainty and are compared through a cross validation analysis. In addition to local uncertainty measures, the probabilities of not exceeding or exceeding any grain size value at any locations are retrieved and mapped from the local ccdf models. A case study of Baramarae beach, Korea is carried out to illustrate the potential of geostatistical uncertainty modeling.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.71-84
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• 2017

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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.1
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• pp.13-21
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• 2020

Surrogate models have been used for the rapid estimation of six-DOF aerodynamic coefficients in the context of the design and control of a missile. For this end, we may generate highly accurate surrogate models with a multitude of aerodynamic data obtained from wind tunnel tests (WTTs); however, this approach is time-consuming and expensive. Thus, we aim to swiftly predict aerodynamic coefficients via co-Kriging using a few WTT data along with plenty of computational fluid dynamics (CFD) data. To demonstrate the excellence of co-Kriging models based on both WTT and CFD data, we first generated two surrogate models: co-Kriging models with CFD data and Kriging models without the CFD data. Afterwards, we carried out numerical validation and examined predictive trends to compare the two different surrogate models. As a result, we found that the co-Kriging models produced more accurate aerodynamic coefficients than the Kriging models thanks to the assistance of CFD data.

• Journal of the Korean earth science society
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• v.32 no.3
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• pp.249-264
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• 2011

A multi-Gaussian kriging approach extended to space-time domain is presented for uncertainty modeling as well as time-series mapping of environmental variables. Within a multi-Gaussian framework, normal score transformed environmental variables are first decomposed into deterministic trend and stochastic residual components. After local temporal trend models are constructed, the parameters of the models are estimated and interpolated in space. Space-time correlation structures of stationary residual components are quantified using a product-sum space-time variogram model. The ccdf is modeled at all grid locations using this space-time variogram model and space-time kriging. Finally, e-type estimates and conditional variances are computed from the ccdf models for spatial mapping and uncertainty analysis, respectively. The proposed approach is illustrated through a case of time-series Particulate Matter 10 ($PM_{10}$) concentration mapping in Incheon Metropolitan city using monthly $PM_{10}$ concentrations at 13 stations for 3 years. It is shown that the proposed approach would generate reliable time-series $PM_{10}$ concentration maps with less mean bias and better prediction capability, compared to conventional spatial-only ordinary kriging. It is also demonstrated that the conditional variances and the probability exceeding a certain thresholding value would be useful information sources for interpretation.

• Journal of the Korean Geographical Society
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• v.44 no.3
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• pp.395-409
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• 2009

The objective of this paper is to illustrate that kriging can provide an effective framework both for integrating remote sensing data and for uncertainty modeling through a case study of sediment grain size mapping with remote sensing data. Landsat TM data which show reasonable relationships with grain size values are used as secondary information for sediment grain size mapping near the eastern part of Anmyeondo and Cheonsuman bay. The case study results showed that uncertainty attached to prediction at unsampled locations was significantly reduced by integrating remote sensing data through the analysis of conditional variance from conditional cumulative distribution functions. It is expected that the kriging-based approach presented in this paper would be efficient integration and analysis methodologies for any environmental thematic mapping using secondary information as well as sediment grain size mapping.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• Journal of the Korean Geotechnical Society
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• v.28 no.1
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• pp.17-27
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• 2012

Since geologic data are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations but also to assess the uncertainty attached to the prediction. In this study the assessment of the local uncertainty of prediction for the thickness of the consolidation layer was performed by using the indicator approach. A conditional cumulative distribution function (ccdf) was first modeled, and then E-type estimates and the conditional variance were computed for the spatial distribution of the thickness of the consolidation layer. These results could be used to estimate the spatial distribution of secondary compression and to assess the local uncertainty of secondary compression for Songdo New City.