• 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.

• Journal of the Korean Geographical Society
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• v.49 no.5
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• pp.761-778
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• 2014

There has been relatively little study to model price for commercial property because of its low transaction volume in the market. Despite of this thin market character, this paper tried to estimate prices for commercial lots as accurate as possible. We constructed a model whose components consist of mean structure(global trend), exponential covariance function and a pure error term, and applied it to actual sales price data of Seoul. We explicitly took account of spatial autocorrelation of land price by utilizing a kriging technique, a representative method of spatial interpolation, because the land price of commercial lots has feature of differential price forming pattern depending on submarkets they belong to. In addition, we chose to apply a bayesian kriging to overcome data scarcity by incorporating experts' knowledge into prior probability distribution. The chosen model's excellent performance was verified by the result from validation data. We confirmed that the excellence of the model is attributed to incorporating both autocorexperts' knowledge and spatial autocorrelation in the model construction. This paper is differentiated from previous studies in the sense that it applied the bayesian kriging technique to estimate price for commercial lots and explicitly combined experts' knowledge with data. It is expected that the result of this paper would provide a useful guide for the circumstances under which property price has to be estimated reliably based on sparse transaction data.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.43-51
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• 2012

Due to the increasing demand to utilize water fronts and water resource effectively, a multi-dimensional model that provides detailed hydraulic characteristics is required in order to improve the decision making process. An EFDC model is a kind of multi-dimension model, and it requires detailed 3D (3-dimensional) terrain in order to simulate the hydraulic characteristics of stream flow. In the case of 3D terrain creation, especially river reaches, measurement resolution and spatial interpolation methods affect the detailed 3D topography which uses input data for EFDC simulation. Such results make hydraulic characteristics to be varied. This study aims to examine EFDC simulation results depending on the 3D topographies derived by separate measurement resolution and spatial interpolation methods. The study area is at the confluence of the Nakdong and Kuemho Rivers and the event rain implemented was Typhoon Ewiniar in 2006. As a result, in the case of the area-elevation curve, the difference by means of the interpolation methods was significant when applying the same measurement resolution, except at 160m resolution. Furthermore, when the measurement resolution was 80m or above, the difference in a cross-section was occurred. Meanwhile, the water level changes between interpolation methods were insignificant by the measurement resolution except when the Kriging method was used for the 160m measurement data. Velocity changes emerged according to the interpolation methods when measurement resolution was 80m or above and the Kriging method resulted in a velocity that had a considerable gap in relation to the results from other methods at a measurement resolution of 160m.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2006.04a
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• pp.391-393
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• 2006

In this study, the performance of artificial neural network (ANN) models for estimating spatial distribution of water quality was evaluated using electric conductivity (EC) values in landfill site. For the ANN model development, feedforward neural networks and backpropagation algorithm with gradient descent method were used. In Test 1, the interpolation ability of the ANN model was evaluated. Results of the ANN model were more precise than those of the Kriging model. In Test 2, spatial distributions of EC values were predicted using precipitation data. Results seemed to be reasonable, however, they showed a limitation of ANN models in extrapolations.

• Economic and Environmental Geology
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• v.30 no.5
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• pp.493-498
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• 1997

The purpose of this study is to develop geostatistical model for evaluating the abundance of deep-sea manganese nodule. The abundance data used in this study were obtained from the KODOS (Korea Deep Ocean Study) area. The variation of nodule abundance was very high within short distance, while sampling methods was very limited. As the distribution of nodule abundance showed non-gaussian, indicator simulation method was used instead of conditional simulation method and/or ordinary kriging. The abundance data were encoded into a series of indicators with 6 cutoff values. They were used to estimate the conditional probability distribution function (cpdf) of the nodule abundance at any unsampled location. The standardized indicator variogram models were obtained according to variogram analysis. This SIS method had the advantage over other traditional techniques such as the turning bands method and ordinary kriging. The estimating values by indicator conditional simulation near high abundance area were more detailed than by ordinary kriging and indicator kriging. They also showed better spatial characteristics of distribution of nodule abundance.

• Environmental Engineering Research
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• v.24 no.1
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• pp.150-158
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• 2019

Soil contaminated with heavy metals from artisanal gold mining in Anka Local Government Area in Northwestern Nigeria was investigated to evaluate the human health risk as a result of heavy metals. Measured concentration of heavy metals and exposure parameters were used to estimate human carcinogenic and non-carcinogenic risk. GIS-based Kriging method was utilized to create a prediction maps of human health risks and probability maps of heavy metals concentrations exceeding their threshold limits. Hazard index calculation showed that 21 out of 23 locations are posing non-cancer risk for children. Adults and children are at high cancer risk in all locations as the total cancer risk exceeded $1{\times}10^{-6}$ (the lower limit CTR value). Kriging model showed that only a very small area in Anka has a hazard index of less than unity and cumulative target risk of less than $1{\times}10^{-4}$, indicating a significant carcinogenic and non-carcinogenic risks for children. The probability of heavy metals to exceed their threshold concentrations around the study area was also found to be high.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.530-537
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• 2012

In the recent past, genetic algorithm (GA) and evolutionary optimization scheme have become increasingly popular for the design of electromagnetic (EM) devices. However, the conventional GA suffers from computational drawback and parameter dependency when applied to a computationally expensive problem, such as practical EM optimization design. To overcome these issues, a hybrid optimization scheme using GA in conjunction with Kriging is proposed. The algorithm is validated by using two mathematical problems and by optimizing rotor structure of interior permanent magnet synchronous machine.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.10 s.103
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• pp.1123-1130
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• 2005

We propose an optimal design to improve the capacity by reducing the noise of the intake system. The length and radius of each component of the current Intake system are selected as control factors. We accept the output from computer simulator with orthogonal arrays. Then, the Kriging estimates are computed. From this, we exploit the optimal design of the intake system by adapting simulated annealing. From the results of this optimal design, we conclude that Kriging method with the orthogonal arrays is the efficient method for Design and Analysis of Computer Experiments and we propose the useful results for the low noise intake system.

• Journal of Environmental Impact Assessment
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• v.18 no.1
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• pp.31-39
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• 2009

A lot of data which are used in environment analysis of air pollution have characteristics that are distributed continuously in space. In this point, the collected data value such as precipitation, temperature, altitude, pollution density, PM10 have spatial aspect. When geostatistical data analysis are needed, acquisition of the value in every point is the best way, however, it is impossible because of the costs and time. Therefore, it is necessary to estimate the unknown values at unsampled locations based on observations. In this study, spatial interpolation method such as local trend surface model, IDW(inverse distance weighted), RBF(radial basis function), Kriging were applied to PM10 annual average concentration of Seoul in 2005 and the accuracy was evaluated. For evaluation of interpolation accuracy, range of estimated value, RMSE, average error were analyzed with observation data. The Kriging and RBF methods had the higher accuracy than others.