• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.141-141
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• 2016

미계측 지역에서의 토양수분을 예측하기 위하여 조건부합성방법을 한반도에 적용하여 비교 분석하였다. 토양수분 자료는 농촌진흥청에서 제공하는 지상관측 자료와 GCOM-W1 위성의 Advanced Microwave Scanning Radiometer2 (AMSR2) 센서의 자료를 사용하였다. 위성관측 토양수분자료의 오차를 제거하기 위하여 지상관측자료에 정규화 하였고, 정규화된 위성관측 자료와 지상관측자료를 조건부합성 방법을 이용하여 합성하였다. 조건부 합성방법의 성능을 평가하기 위하여 leave-one-out 교차검증 방법을 사용하였고, 분석 결과 지상관측자료에 위성자료를 합성한 조건부합성방법이 지상관측자료만을 사용한 크리깅 방법에 비해 우세하게 나타났다. 또한 각 관측지점에서의 조건부합성 방법을 이용한 토양수분 예측 성능을 공간분포 시켜 지역적인 특성을 분석한 결과 관측소의 밀도와 지형적인 특성이 조건부합성방법의 성능에 영향을 미치는 것으로 나타났다. 본 연구의 결과는 원격탐사기법으로 관측된 토양수분 자료의 공간적인 특성을 고려하여 지상 관측 자료와 합성하는 것이 토양수분 공간보간성능을 향상 시킬 수 있다는 것을 의미한다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.101-104
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• 2009

최적설계는 설계자가 요구하는 제한조건을 만족시키는 범위에서 목적함수가 최소가 되는 설계점을 찾는 방법이다. 그러나 기존의 최적설계는 불확실성의 영향을 고려하지 않아 최적해가 제한조건의 경계에 위치하고 이것은 모델링과정이나 가공 등으로 인한 오차에 대한 영향을 고려하지 않는 문제점이 있다. 신뢰성 기반 최적설계는 불확실성을 정량화하면서 신뢰도를 계산하는 신뢰도 해석과정과 최적설계과정을 포함한다. 일반적으로 신뢰성 해석은 크게 추출법, 급속 확률 적분법, 모멘트 기반 신뢰성해석이 있다. 가장 널리 사용되는 급속 확률 적분법 중 최대 손상 가능점(MPP) 방법은 많은 MPP점이 존재하는 경우 수치적 비용이 증가하는 문제점과 표준 정규분포 공간으로 변환하는 과정에서 제한조건의 비선형성을 증가시켜 큰 오차를 발생시키는 문제점이 있다. 본 논문에서는 RBDO를 수행하기에 앞서 선행되어야 할 신뢰성해석 방법으로 곱분해기법을 사용하였고 이로부터 민감도 정보를 유도하여 기울기 기반 최적화 알고리즘을 적용하였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.849-856
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• 2006

This paper is to examine the applicability of ordinary Kriging interpolation(OK) to the p-adaptivity of the finite element analysis that is based on variogram. In the p-refinement, the analytical domain has to be refined automatically to obtain an acceptable level of accuracy by increasing the p-level non-uniformly or selectively. In case of non-uniform p-distribution, the continuity between elements with different polynomial orders is achieved by assigning zero higher-order derivatives associated with the edge in common with the lower-order derivatives. It is demonstrated that the validity of the proposed approach by analyzing results for stress singularity problem.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.715-727
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• 2023

To provide detailed and appropriate meteorological information in mountainous areas, the Korea Forest Service has established an Automatic Mountain Meteorology Observation Station (AMOS) network in major mountainous regions since 2012, and 464 stations are currently operated. In this study, we proposed an optimal kriging technique with lapse rate correction to produce gridded temperature data suitable for Korean forests using AMOS point observations. First, the outliers of the AMOS temperature data were removed through statistical processing. Then, an optimized theoretical variogram, which best approximates the empirical variogram, was derived to perform the optimal kriging with lapse rate correction. A 500-meter resolution Kriging map for temperature was created to reflect the elevation variations in Korean mountainous terrain. A blind evaluation of the method using a spatially unbiased validation sample showed a correlation coefficient of 0.899 to 0.953 and an error of 0.933 to 1.230℃, indicating a slight accuracy improvement compared to regular kriging without lapse rate correction. However, the critical advantage of the proposed method is that it can appropriately represent the complex terrain of Korean forests, such as local variations in mountainous areas and coastal forests in Gangwon province and topographical differences in Jirisan and Naejangsan and their surrounding forests.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.2
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• pp.146-156
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• 2010

The objective of this study was to improve the accuracy of calculation and estimation of solar radiation and duration of sunshine, which are the most important variables of photovoltaic power generation in deciding the location of photovoltaic facilities efficiently. With increasing interest in new and renewable energies, research on solar energy is also being conducted actively, but there have not been many studies on the location of photovoltaic facilities. Thus, this study calculated solar duration and solar radiation based on geographical factors, which have the most significant effect on solar energy in GIS environment, and corrected the results of analysis using diffuse radiation. Moreover, we performed ordinary kriging, a spatial statistical analysis method, for estimating values for parts deviating from the spatial resolution of input data, and used variogram, which can determine the spatial interrelation and continuity of data, in order to estimate accurate values. In the course, we compared the values of variogram factors and estimates from applicable variogram models, and selected the model with the lowest error rate. This method is considered helpful to accurate decision making on the location of photovoltaic facilities.

• 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.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.11
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• pp.1305-1310
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• 2012

The performance of a system can be affected by the variance of noise factors, which arise owing to uncertainties of the material properties and environmental factors acting on the system. For robust design optimization of the system performance, it is necessary to minimize the effect of the variance of the noise factors that are impossible to control. However, present robust design techniques consider the variation of design factors, and not the noise factors, as being important. Furthermore, it is necessary to assume a normal distribution; however, a normal distribution is often not suitable to estimate the variations. In this study, a robust design technique is proposed to consider the variation of noise factors that are estimated as non-normal distributions in a real experiment. As an example of an engineering problem, a deep-sea manganese nodule miner tracked vehicle is used to demonstrate the feasibility of the proposed method.

• Economic and Environmental Geology
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• v.52 no.6
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• pp.573-586
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• 2019

A seismic hazard map based on spatial analysis of various sources of geologic seismic information was developed and assessed for regional seismic vulnerability in South Korea. The indicators for assessment were selected in consideration of the geological characteristics affecting the seismic damage. Probabilistic seismic hazard and fault information were used to be associated with the seismic activity hazard and bedrock depth related with the seismic damage hazard was also included. Each indicator was constructed of spatial information using GIS and geostatistical techniques such as ordinary kriging, line density mapping and simple kriging with local varying means. Three spatial information constructed were integrated by assigning weights according to the research purpose, data resolution and accuracy. In the case of probabilistic seismic hazard and fault line density, since the data uncertainty was relatively high, only the trend was intended to be reflected firstly. Finally, the seismic activity hazard was calculated and then integrated with the bedrock depth distribution as seismic damage hazard indicator. As a result, a seismic hazard map was proposed based on the analysis of three spatial data and the southeast and northwest regions of South Korea were assessed as having high seismic hazard. The results of this study are expected to be used as basic data for constructing seismic risk management systems to minimize earthquake disasters.