• Communications for Statistical Applications and Methods
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• v.27 no.5
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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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.219-221
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• 2003

With regarding to web GIS, OGC promotes WFS allowing a client to retrieve geospatial data encoded in GML which is a modeling language to encode the semantics, syntax and schema of geospatial information resources. Even though GML provides benefits for geographic description, it is too heavy to be processed by mobile devices. In order to address the issue, this paper evaluates GML service with WFS server and GML viewers. Through this paper, we get analyses of properties of GML geospatial data and the effects on wireless devices, which are expected to be fundamental materials onto a design of mobile applications.

• Geomechanics and Engineering
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• v.11 no.1
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• pp.41-58
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• 2016

This paper focuses on the deformation behavior of tunnels crossing a weak zone in conventional tunneling. A three-dimensional finite element model was adopted that allows realistic modeling of the tunnel excavation and the support installation. Using the 3D FE model, a parametric study was conducted on a number of tunneling cases with emphasis on the spatial characteristics of the weak zone such as the strike and dip angle, and on the initial stress state. The results of the analyses were thoroughly examined so that the three-dimensional tunnel displacements at the tunnel crown and the sidewalls can be related to the spatial characteristic of the weak zone as well as the initial stress state. The results indicate that the effectiveness of the absolute displacement monitoring data as early warning indicators depends strongly on the spatial characteristics of the weak zone. It is also shown that proper interpretation of the absolute monitoring data can provide not only early warning for a weak zone outside the excavation area but also information on the orientation and the extent of the weak zone. Practical implications of the findings are discussed.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.3
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• pp.249-261
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• 2003

A three-dimensional mesoscale atmospheric dispersion modeling system consisting of the Lagrangian particle dispersion model (LPDM) and the meteorological mesoscale model (MM5) was employed to simulate the transport and dispersion of non-reactive pollutant during the nuclear spill event occurred from Sep. 31 to Oct. 3, 1999 in Tokaimura city, Japan. For the comparative analysis of numerical experiment, two more sets of foreign mesoscale modeling system; NCEP (National Centers for Environmental Prediction) and DWD (Deutscher Wetter Dienst) were also applied to address the applicability of air pollution dispersion predictions. We noticed that the simulated results of horizontal wind direction and wind velocity from three meteorological modeling showed remarkably different spatial variations, mainly due to the different horizontal resolutions. How-ever, the dispersion process by LPDM was well characterized by meteorological wind fields, and the time-dependent dilution factors ($\chi$/Q) were found to be qualitatively simulated in accordance with each mesocale meteorogical wind field, suggesting that LPDM has the potential for the use of the real time control at optimization of the urban air pollution provided detailed meteorological wind fields. This paper mainly pertains to the mesoscale modeling approaches, but the results imply that the resolution of meteorological model and the implementation of the relevant scale of air quality model lead to better prediction capabilities in local or urban scale air pollution modeling.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.129-143
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• 2012

This paper demonstrates the effectiveness of advanced spatial modeling techniques for environmental monitoring and impact assessment through a case study of Chernobyl nuclear accident occurred in 1986. Land-cover types changed after the accident are analysed by a post classification comparison method using bi-temporal Landsat TM data acquired in 1986 and 1992 near the accident site. Spatial modeling including various kriging algorithms are also applied to analyze the relationships between Cesium concentrations in soil and thyroid cancer incidence rates in Belarus, which was greatly damaged by the accident. The change detection results clearly showed the decrease of croplands and the increase of abandoned lands, and concrete structures were newly built around the nuclear plant to prevent the spread of radioactive contamination. In Belarus, high Cesium concentrations were observed in southern areas with high thyroid cancer risk estimated by Poisson kriging. Geographically weighted regression, which could account for geographic variations of independent variables including Cesium concentrations and distances from the Chernobyl nuclear power plant, was applied to extract the relationships between the independent variables and the thyroid cancer risk. The estimated risk values showed a correlation coefficient value of 0.98 with respect to the thyroid cancer risk values, which implied that the thyroid cancer risk in Belarus was affected by the accident. In conclusion, it is expected that advanced spatial modeling techniques applied in this study would be useful for environmental impact assessment and public health research.

• Journal of KSNVE
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• v.7 no.1
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• pp.135-142
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• 1997

This paper presents a digital modeling technique of the distributed system. The basic idea of the proposed technique is to discretize a continuous system with respect to the spatial coordinates using bilinear method. The response of the discretized system is analyzed by Laplace transform and z-transform. The computational results in torsional shaft and Timoshenko beam using the proposed technique are compared with the exact solutions and the results of finite element method.

• Proceedings of the Korean Society of Crop Science Conference
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• 2000.11a
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• pp.116-117
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• 2000

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.471-472
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• 2010

In this study, a new modeling framework for predicting temperature and structural behaviors of structures under fire condition is proposed. The proposed modeling framework including fire simulation, heat transfer and structural analysis is applied to simulate fire tests performed on the steel-concrete composite structures in Cardington, UK, for model validations. Good predictions are shown for spatial-temporal temperatures and deflections of fire-damaged steel-concrete structures.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.89-99
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• 2018

Spatial downscaling is often applied to coarse scale satellite products with high temporal resolution for environmental monitoring at a finer scale. An area-to-point regression kriging (ATPRK) algorithm is regarded as effective in that it combines regression modeling and residual correction with area-to-point kriging. However, an open source tool or package for ATPRK has not yet been developed. This paper describes the development and code organization of an R-based spatial downscaling tool, named R4ATPRK, for the implementation of ATPRK. R4ATPRK was developed using the R language and several R packages. A look-up table search and batch processing for computation of ATP kriging weights are employed to improve computational efficiency. An experiment on spatial downscaling of coarse scale land surface temperature products demonstrated that this tool could generate downscaling results in which overall variations in input coarse scale data were preserved and local details were also well captured. If computational efficiency can be further improved, and the tool is extended to include certain advanced procedures, R4ATPRK would be an effective tool for spatial downscaling of coarse scale satellite products.