• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.168-178
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• 2006

In recent years, an ingress of mass jellyfish into cooling water intake system causes interruption of electric power production at the Uljin nuclear power plant. Therefore, monitering and forecast on the mass ingress of marine organisms are demanded as one of the early preventing measurements. Sea water movement is a major factor on the ingress of marine organisms like Moon jellyfish which has weak self-mobile ability. When sea surface flow direction adjacent to the Uljin is the northwest, the jellyfish on the Tsushima warm currents move to the Uljin power plant. To detect the direction of sea surface warm flows, the spatial range with $25km{\times}25km$ is set up and NOAA sea surface temperature(SST) data are collected in this area. For the statistical analysis, the SST data are made as GIS point data and geostatistical analysis of ArcGIS is used. Analyzing directional semivariogram, the anisotropy of the SST point data are calculated and warm flow direction is detected. This experimental results are expected to use as an element technology for the early warning system development of mass jellyfish ingress in power plant.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.2
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• pp.140-148
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• 2007

The remote sensing observations of land surface properties are inevitably influenced by the landscape heterogeneity. In this paper, we introduce a geostatistical technique to provide a quantitative interpretation of landscape heterogeneity in terms of key land surface parameters. The study areas consist of the two KoFlux sites: (1) the Gwangneung site, covered with temperate mixed forests on a complex terrain, and (2) the Haenam site with mixed croplands on a relatively flat terrain. The semivariogram and fractal analyses were performed for both sites to characterize the spatial heterogeneity of two radiation parameters, i.e., land surface temperature (LST) and albedo. These parameters are the main factors affecting the reflected longwave and shortwave radiation components from the two study sites. We derived them from the high-resolution Landsat ETM+ satellite images collected on 23 Sep. 2001 and 14 Feb. 2002. The results of our analysis show that the characteristic scales of albedo was >1 km at the Gwangneung site and approximately 0.3 km at the Haenam site. For LST, the scale of heterogeneity was also >1 km at the Gwangneung site and >0.6 to 1.0 km at the Haenam site. At both sites, there was little change in the characteristic scales of the two parameters between the two different seasons.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.445-456
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• 2003

Spatial data are usually obtained at selected locations even though they are potentially available at all locations in a continuous region. Moreover the monitoring locations are clustered in some regions, sparse in other regions. One important goal of spatial data analysis is to predict unknown response values at any location throughout a region of interest. Thus, an appropriate space model should be set up and their estimates and predictions must be accompanied by measures of uncertainty. In this study we see that a space model proposed allows a best interpolation to annual rainfall data in South Korea.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.295-300
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• 2003

공간적으로 영향을 받는 위치에서의 상호 연관성을 고려한 예측모형 중에서 크리깅 (kriging) 방법은 관측된 데이터를 보간(interpolation)하고, 부드럽게 연결(smoothing)하며, 새로운 데이터를 예측(prediction)하는 통계적 모형으로서 많이 활용되고 있다. 크리깅 모형을 적용하기 위해서는 먼저 주어진 두 위치에서의 비연관성을 나타내는 세미베리오그램 (semivariogram)의 3가지 모수(nugget, sill, range)를 추정해야 한다. 본 연구에서는 전역 적 최적화 방법인 유전자 알고리즘(genetic algorithm)을 도입하여 세미베리오그램 모수들을 추정하였고, 이를 통해 강우량(rainfall)에 대한 크리깅 추정량을 산출하고 효과성을 판단하였다.

• The Korean Journal of Applied Statistics
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• v.12 no.1
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• pp.125-141
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• 1999

서로 다른 위치에서 동시에 관찰된 자료들이 공간적인 변인에 의하여 영향을 받는다면 공간적인 변인의 함수식에 의한 예측모형을 설정하는 것이 타당하다. 본 연구에서는 공간적인 변인으로 거리가 주어졌을 때, 공간자료에 대한 세미베리오그램 모형의 추정과 관측되지 않은 지점에 대한 공간예측기법을 정리하였으며, 또한 공간이상치 탐지를 위한 두가지 방법론으로 분포론적 방법과 p-Deletion 방법을 제시하였다. 방법론의 예시를 위하여 강우강도 자료를 이용하였으며 서로 상관되어 있는 공간데이터에 대한 시뮬레이션을 통하여 두가지 방법을 비교하였다.

• Economic and Environmental Geology
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• v.26 no.2
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• pp.207-215
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• 1993

In modem petroleum reservoir engineering, the characterization of reservoir heterogeneities is very important to accurately understand and predict reservoir production performance. Formation evaluation for the description of reservoir is generally conducted by performing the analysis of well logging, core testing, and well testing. However, the measured data points by well logging or core testing are in general very sparse and hence reservoir properties should be interpolated and extrapolated from measured points to uncharacterized areas. In assigning the data for the unknown points, simple averaging technique is not feasible as optimum estimation method since this method does not account the spatial relationship between the data points. The main goal of this work is to develop PC-version of multi-purpose geostatistical model in which several stages are systematically proceeded. In the development of model, the simulator employs a automatic selection of semivariogram function such as exponential or spherical model with the best values of $R^2$. The simulator also implements a special algorithm for the fitting of semivariogram function to experimental sernivariogram. The special algorithm such as trial and error scheme is devised since this method is much more reliable and stable than Gauss-Newton method. The simulator has been tested under stringent conditions and found to be stable. Finally, the validity and the applicability of the developed model have been studied against some existing actual field data.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.555-563
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• 2016

Uniformity and leveled distributions of soil chemicals across paddy fields are critical to manage optimal crop yields, reduce environmental risks and efficiently use water in rice cultivation. In this study, an investigation of spatial distributions on soil chemical properties was conducted to evaluate the effect of land leveling on mitigation of soil chemical property heterogeneity from a remodeled paddy field. The spatial variabilities of chemical properties were analyzed by geostatistical analyses; semivariograms and kriged simulations. The soil samples were taken from a 1 ha paddy field before and after land leveling with sufficient water. The study site was located at Bon-ri site of Dalseong and river sediments were dredged from Nakdong river basins. The sediments were buried into the paddy field after 50 cm of top soils at the paddy field were removed. The top soils were recovered after the sediments were piled up. In order to obtain the most accurate spatial field information, the soil samples were taken at every 5 m by 5 m grid point and total number of samples was 100 before and after land leveling with sufficient water. Soil pH increased from 6.59 to 6.85. Geostatistical analyses showed that chemical distributions had a high spatial dependence within a paddy field. The parameters of semivariogram analysis showed similar trends across the properties except pH comparing results from before and after land leveling. These properties had smaller "sill" values and greater "range" values after land leveling than ones from before land leveling. These results can be interpreted as land leveling induced more homogeneous distributions of soil chemical properties. The homogeneous distributions were confirmed by kriged simulations and distribution maps. As a conclusion, land leveling with sufficient water may induce better managements of fertilizer and water use in rice cultivation at disturbed paddy fields.

• Journal of Mechanical Science and Technology
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• v.16 no.5
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• pp.619-632
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• 2002

In recent study on design of experiments, the complicate metamodeling has been studied because defining exact model using computer simulation is expensive and time consuming. Thus, some designers often use approximate models, which express the relation between some inputs and outputs. In this paper, we review and compare the complicate metamodels, which are expressed by the interaction of various data through trying many physical experiments and running a computer simulation. The prediction model in this paper employs interpolation schemes known as ordinary kriging developed in the fields of spatial statistics and kriging in Design and Analysis of Computer Experiments (DACE) model. We will focus on describing the definitions, the prediction functions and the algorithms of two kriging methods, and assess the error measures of those by using some validation methods.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.19-30
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• 2003

Spatiotemporal statistical models are used for analyzing space-time data in many fields, such as environmental sciences, meteorology, geology, epidemiology, forestry, hydrology, fishery, and so on. It is well known that classical spatiotemporal process modeling requires the estimation of space-time variogram or covariance functions. In practice, the estimation of such variogram or covariance functions are computationally difficult and highly sensitive to data structures. We investigate a Bayesian hierarchical model which allows the specification of a more realistic series of conditional distributions instead of computationally difficult and less realistic joint covariance functions. The spatiotemporal model investigated in this study allows both spatial component and autoregressive temporal component. These two features overcome the inability of pure time series models to adequately predict changes in trends in individual sites.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.1
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• pp.63-73
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• 1991

The data type of the echo sounding for the contouring of coastal chart is continuous profiles, and there are no data between the profiles. In this study, at first, the depths of the regular grid along the sounding line were interpolated by linear equation. After that the depths of the regular grid located between the profiles were interpolated by kriging. The semivariogram contributes to the weight of interpolation. After comparison with the conventional Moving Average and Kriging, it turns out that this algorithm shows merits in the field of the accuracy and computing time.