• Spatial Information Research
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• 제19권4호
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• pp.81-89
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• 2011

이 논문에서는 공간적으로 소수의 지점에서 획득된 현장 조사 자료의 공간 보간 과정에 범주형 자료를 결합하는 다변량 크리깅 기법을 제안하고자 한다. 범주형 자료를 결합하는 과정에서 기존 범주형 자료의 속성별로 대푯값을 할당하는 단일 지역 평균 기반의 단순 크리깅 방식 대신에, 영역-점 변환 크리깅을 이용하여 원하는 해상도로 상세화시킨 추정값을 가변적 지역 평균으로 이용하였다. 지화학 원소 구리의 공간 보간에 지질도를 이용하는 사례연구를 통해 제안 기법을 예시하였다. 교차 검증 결과, 제안 기법이 단변량 정규 크리깅과 기존 단일 지역 평균 기반의 단순 크리깅 기법에 비해 각각 15%와 25%의 예측 능력의 향상을 나타내었다. 따라서 범주형 자료를 부가 자료로 이용하는 공간 보간에 이 논문에서 제안한 기법이 효율적으로 적용될 수 있을 것으로 기대된다.

• 한국환경복원기술학회지
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• 제22권4호
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• pp.1-14
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• 2019

The 1st Forest Health Management survey was conducted to examine the health of the forests in Korea. However, in order to understand the health of the forests, which account for 63.7% of the total land area in South Korea, it is necessary to comprehensively spatialize the results of the survey beyond the sampling points. In this regard, out of the sample points of the 1st Forest Health Management survey in Gyeongbuk area, 78 spots were selected. For these spots, the species diversity index was selected from the survey sections, and the spatial interpolation method was applied. Inverse distance weighted (IDW), Ordinary Kriging and Ordinary Cokriging were applied as spatial interpolation methods. Ordinary Cokriging was performed by selecting vegetation indices which are highly correlated with species diversity index as a secondary variable. The vegetation indices - Normalized Differential Vegetation Index(NDVI), Leaf Area Index(LAI), Sample Ratio(SR) and Soil Adjusted Vegetation Index(SAVI) - were extracted from Landsat 8 OLI. Verification was performed by the spatial interpolation method with Mean Error(ME) and Root Mean Square Error(RMSE). As a result, Ordinary Cokriging using SR showed the most accurate result with ME value of 0.0000218 and RMSE value of 0.63983. Ordinary Cokriging using SR was proven to be more accurate than Ordinary Kriging, IDW, using one variable. This indicates that the spatial interpolation method using the vegetation indices is more suitable for spatialization of the biodiversity index sample points of 1st Forest Health Management survey.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2008년도 정기 학술대회
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• pp.160-165
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• 2008

Kriging interpolation technique has been proposed by Danny Krige of South Africa to find the mineral distribution grade from information of geography and space. It is one of the generally used prediction technique for the mineral distribution grade and underground water level in wide scope also used in computer graphics fields by the ability for the surface regeneration This paper comprises two specific objectives. The first is to examine the applicability of Ordinary Kriging interpolation(OK) to finite element method that is based on variogram modeling in conjunction with different allowable limits of separation distance. The second is to investigate the accuracy according to theoretical variogram such as polynomial, Gauss, and spherical models.

• 한국농공학회논문집
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• 제53권5호
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• pp.1-7
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• 2011

The objective of this study is to decide a spatial interpolation method on temperature data for the suitability analysis of garlic cultivation. In Korea, garlic is the second most cultivated condiment vegetable after red pepper. Nowadays warm-temperate garlic faces potential shift of its arable area according to warmer temperature in the Korean Peninsula, and the change can be drawn with the precise temperature map derived from interpolation on point-measured data. To find the preferable interpolation method in cases of germination and vegetative period of the garlic, different approaches were tested as follows: Inverse Distance Weighted (IDW), Spline, Ordinary Kriging (OK), and Universal Kriging (UK). As a result, IDW and UK show the lowest root mean square errors as for the germination and vegetative seasons, respectively. However, statistically significant difference was not revealed among the applied methods regarding the germinating period. Eventually this will contribute to mapping the suitable lands for the cultivation of warm-temperate garlic reasonably.

• Water Engineering Research
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• 제5권3호
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• pp.123-131
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• 2004

This paper describes the use of spatial interpolation for estimating seasonal crop potential evapotranspiration (PET) and irrigation water requirement in unmeasured evaporation gage stations within Edwards Aquifer, Texas using GIS. The Edwards Aquifer area has insufficient data with short observed records and rare gage stations, then, the investigation of data for determining of irrigation water requirement is difficult. This research shows that spatial interpolation techniques can be used for creating more accurate PET data in unmeasured region, because PET data are important parameter to estimate irrigation water requirement. Recently, many researchers are investigating intensively these techniques based upon mathematical and statistical theories. Especially, three techniques have well been used: Inverse Distance Weighting (IDW), spline, and kriging (simple, ordinary and universal). In conclusion, the result of this study (Table 1) shows the kriging interpolation technique is found to be the best method for prediction of unmeasured PET in Edwards aquifer, Texas.

• Communications for Statistical Applications and Methods
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• 제27권5호
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• pp.547-568
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• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.

• 지구물리와물리탐사
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• 제16권4호
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• pp.257-267
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• 2013

철광산 지역에서 획득한 시추자료와 물리탐사자료를 복합 분석하여 3차원 광체모델링을 수행하였다. 지질조사 및 시추조사 자료를 통해 획득한 5가지 대표 암종에 지수를 부여하였고, 이를 이용하여 광체의 범위를 효율적으로 결정하기 위해 지구통계학적 순차 지표 시뮬레이션(Sequential Indicator Simulation)을 실시하였다. 그리고 전기비저항 탐사 자료와 자기지전류 탐사 자료를 이용한 부가적인 자료를 생성하기 위해 정규크리깅(Ordinary Kriging)과 순차가우스시뮬레이션(Sequential Gaussian Simulation)을 사용하였다. 시추자료에서 획득한 입력변수와 전기비저항자료 간의 상관관계를 분석하여 지구통계학적 복합 분석에 적용하였다. 상관관계 분석 결과, 밀도가 높아질수록 전기비저항이 낮아지는 관계를 확인할 수 있었으며, 이를 통해서 다변량 크리깅 중 하나인 가변적 지역평균 크리깅(Simple Kriging with Local varying means)을 적용하여 지수를 이용한 광체의 모델과 품위 자료를 이용한 품위 분포 모델을 생성하였다. 광체 모델링 결과, 실제 채굴도와 유사한 결과를 확보할 수 있었고, 품위자료에 대한 모델링 결과는 품위별 위치에 따른 변화 정보를 제공하였다.

• 한국지반공학회논문집
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• 제26권10호
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• pp.39-47
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• 2010

본 논문에서는 분석영역 내 압축지수 분포특성이 압밀침하량의 공간적 분포에 미치는 영향을 알아보기 위해, 압축지수의 분포특성을 고려한 압밀침하 추정방법을 제시하고 추정방법에 따른 압밀침하량 추정 결과를 비교해 보았다. 지반이 불균질한 경우에는 간극비를 이차변수로 이용한 정규공동크리깅이 신뢰할 수 있는 앙축지수 추정결과를 제공하는 것으로 관찰되었으며 이는 감소된 smoothing effect로 인한 것이다. 압축지수와 간극비의 공간적 분포를 고려하는 경우(Case-l)와 모든 지반물성치의 평균값을 쓰는 경우(Case-2) 두 방법은 압밀침하량의 공간적 분포를 상당히 다르게 평가하며, Case-1이 Case-2에 비해 거리에 따른 압밀침하량의 변화가 상대적으로 큰 것으로 나타났다. Case-1의 경우 압밀침하의 공간적 분포는 압밀층 두께뿐만 아니라 압축지수의 분포에도 영향을 받는 반면 Case-2의 경우 압밀층의 두께 분포에 가장 큰 영향을 받는 것으로 나타났다.

• 한국응용곤충학회:학술대회논문집
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• 한국응용곤충학회 2003년도 추계 학술발표회
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• pp.71-71
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• 2003

• Structural Engineering and Mechanics
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• 제66권6호
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• pp.677-691
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• 2018

Reliability assessment of complex structures using simulation methods is time-consuming. Thus, surrogate models are usually employed to reduce computational cost. AK-MCS is a surrogate-based Active learning method combining Kriging and Monte-Carlo Simulation for structural reliability analysis. This paper proposes three modifications of the AK-MCS method to reduce the number of calls to the performance function. The first modification is related to the definition of an initial Design of Experiments (DoE). In the original AK-MCS method, an initial DoE is created by a random selection of samples among the Monte Carlo population. Therefore, samples in the failure region have fewer chances to be selected, because a small number of samples are usually located in the failure region compared to the safe region. The proposed method in this paper is based on a uniform selection of samples in the predefined domain, so more samples may be selected from the failure region. Another important parameter in the AK-MCS method is the size of the initial DoE. The algorithm may not predict the exact limit state surface with an insufficient number of initial samples. Thus, the second modification of the AK-MCS method is proposed to overcome this problem. The third modification is relevant to the type of regression trend in the AK-MCS method. The original AK-MCS method uses an ordinary Kriging model, so the regression part of Kriging model is an unknown constant value. In this paper, the effect of regression trend in the AK-MCS method is investigated for a benchmark problem, and it is shown that the appropriate choice of regression type could reduce the number of calls to the performance function. A stepwise approach is also presented to select a suitable trend of the Kriging model. The numerical results show the effectiveness of the proposed modifications.