• Spatial Information Research
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• v.14 no.1 s.36
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• pp.29-41
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• 2006

Geostatistical data are obtained only at selected sites even though they are potentially available at any location In a continuous surface. Therefore it is necessary to estimate the unknown values at unsampled locations based on observations. In this study we compared the accuracy of 5 spatial interpolation methods: local trend surface, IDW, RBF, ordinary kriging, universal kriging. These interpolation methods were applied to annual rainfall data. As the results of validation tests, universal kriging with gaussian variogram model showed the best accuracy in comparison with other interpolation methods. In the case of kriging, the predicted values were more accurate and within a more narrow range than other methods. In contrast with kriging, local trend surface analysis, IDW and RBF showed the wide range of predicted values and abrupt changes between neighbors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.677-687
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• 2006

Kriging interpolation is one of the generally used interpolation techniques in Geostatistics field. This technique includes the experimental and theoretical variograms and the formulation of kriging interpolation. In contrast to the conventional least square method for stress recovery, kriging interpolation is based on the weighted least square method to obtain the estimated exact solution from the stress data at the Gauss points. The weight factor is determined by variogram modeling for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. In addition to this, the p-level is increased non-uniformly or selectively through a posteriori error estimation based on SPR (superconvergent patch recovery) technique, proposed by Zienkiewicz and Zhu, by auto mesh p-refinement. The cut-out plate problem under tension has been tested to validate this approach. It also provides validity of kriging interpolation through comparing to existing least square method.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.386-398
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• 2019

The transmission capacity of transmission lines is affected by environmental parameters such as ambient temperature, wind speed, wind direction and so on. The environmental parameters can be measured by the installed measuring devices. However, it is impossible to install the environmental measuring devices throughout the line, especially considering economic cost of power grid. Taking into account the limited number of measuring devices and the distribution characteristics of environment parameters and transmission lines, this paper first studies the environmental parameter estimating method of inverse distance weighted interpolation and ordinary Kriging interpolation. Dynamic thermal rating of transmission lines based on IEEE standard and CIGRE standard thermal equivalent equation is researched and the key parameters that affect the load capacity of overhead lines is identified. Finally, the distributed thermal rating of transmission line is realized by using the data obtained from China meteorological data network. The cost of the environmental measurement device is reduced, and the accuracy of dynamic rating is improved.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.586-597
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• 2022

In this study, a model was developed for the estimating the locational thickness information of the upper clay layer to be used for the consolidation vulnerability evaluation in the Nakdong river estuary. To estimate ground layer thickness information, we developed four spatial estimation models using machine learning algorithms, which are RF (Random Forest), SVR (Support Vector Regression) and GPR (Gaussian Process Regression), and geostatistical technique such as Ordinary Kriging. Among the 4,712 borehole data in the study area collected for model development, 2,948 borehole data with an upper clay layer were used, and Pearson correlation coefficient and mean squared error were used to quantitatively evaluate the performance of the developed models. In addition, for qualitative evaluation, each model was used throughout the study area to estimate the information of the upper clay layer, and the thickness distribution characteristics of it were compared with each other.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.353-366
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• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.287-287
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• 2016

최근 기후변화로 인해 돌발성 집중호우가 증가하는 추세로 홍수피해가 발생하고 있는데 이러한 피해를 예방하고 빠른 대처를 위해 강우의 정밀한 관측뿐만 아니라 강우의 정확한 공간 분포 파악에 대한 필요성이 중요하게 대두되고 있다. 그러나 일반적으로 지상우량계의 경우, 공간적인 강우분포 분석에 한계가 존재하여 레이더 강우자료와 함께 활용하는 연구가 진행되어 왔다. 따라서 본 연구에서는 강우장 추정시, 공간보간 기법인 크리깅 기법을 적용하여 강우장을 추정하고 유출 해석을 통해 그 적용성을 확인하고자 하였다. 국내에서 일반적으로 사용되는 크리깅 기법인 OK(Ordinary Kriging), CK(Co-Kriging) 외에도 KED(Kriging with External Drift) 기법을 적용하여 강우장을 추정하고 분포형 수문모형인 $Vflo^{TM}$의 입력자료로 사용하여 유출해석시 정확도를 비교 분석하였다. 추정된 강우장의 정량적 평가 결과, 지상강우만을 이용하는 OK 기법이 가장 우수한 결과를 나타내었다. 하지만 강우의 공간 분포 특성 반영 측면에서는 KED와 CK가 보다 더 좋은 결과를 나타내었다. 또한 유출해석의 경우 지형학적 매개변수 조정에 의한 강우 입력자료의 왜곡을 배제하기 위해 검 보정은 실시하지 않았으며 오차분석 결과에서 KED, CK, OK, Radar 순으로 관측유량을 잘 재현하는 것으로 확인되었다. 본 연구를 통해 공간보간 기법의 수문학적 적용성을 확인하였으며 모형의 검 보정을 통해 수문모형의 입력자료로서 활용성을 가질 수 있을 것으로 판단된다. 또한 이를 통해 생성된 강우장을 활용한다면, 관측망의 밀도가 낮은 지역과 미계측 유역 등에 적용하여 수문시스템해석에 도움이 될 것으로 판단된다.

• Environmental Engineering Research
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• v.22 no.4
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• pp.369-376
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• 2017

The trend analysis of the study was acquired by selecting multiyear monthly groundwater table data and monitors the wells in each sub-district under the study area. The intention of this research was to analyze the outcome of the non-parametric Mann-Kendall test at greater than the significance level which is 95% of groundwater level in Sylhet. The aptitude is effective at two conjunctures where the confidence bounds are 95% and it meets the estimate line of Sen's. To calculate and assess the spatial differences in the inanition of groundwater table, geostatistical methods was applied based on data from 27 groundwater wells during the period from January 1975 to December 2011 which were obtained from a secondary source, Bangladesh Water Development Board. The geographic information system was used to assess the spatial change in order to find the level of groundwater. Cross-validation errors were found within an advisable level in estimating the groundwater depth with different interpolation models of ordinary kriging methods. Finally, surface maps were generated with the best-fitted model. The southeast region was found highly vulnerable from groundwater level point of view. Northern region was detected highest hazard prone area for diverge groundwater using kriging method.

• Journal of Korean Society on Water Environment
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• v.30 no.4
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• pp.409-417
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• 2014

Spatial prediction methods have been useful to determine the variability of water quality in space and time due to difficulties in collecting spatial data across extensive spaces such as watershed. This study compares two kriging methods in predicting BOD concentration on the unmonitored sites in the Geum River Watershed and to assess its predictive performance by leave-one-out cross validation. This study has shown that cokriging method can make better predictions of BOD concentration than ordinary kriging method across the Geum River Watershed. Challenges for the application of cokriging on the spatial prediction of surface water quality involve the comparison of network-distance-based relationship and euclidean-distance-based relationship for the improvement in the predictive performance.

• Korean Journal of Remote Sensing
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• v.7 no.1
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• pp.45-67
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• 1991

An overview is given on stochastic techniques with which remotely sensed data may be used together with ground measurements for purposes of state estimation and prediction. They can explicitly account for spatiotemporal differences in measurement characteristics between ground measurements and remotely sensed data, and are suitable for highly variant space or space-time processes, such as atmosperic processes, which may be viewed as (containing) a random process. For state estimation of static ststems, optimal linear estimation is described. As alternatives, various co-kriging estimation techniques are also described, including simple, ordinary, universal, lognormal, disjunctive, indicator, and Bayesian extersion to simple and lognormal. For illustrative purposes, very simple examples of optimal linear estimation and simple co-kriging are given. For state estimation and prediction of dynamic system, distributed-parameter kalman filter is described. Issues concerning actual implemention are given, and with application potential are described.