• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.2
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• pp.38-50
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• 2008

The site-specific seismic responses and corresponding seismic hazards are influenced mainly by the subsurface geologic and geotechnical dynamic characteristics. To estimate reliably the seismic responses in this study, a geotechnical information system (GTIS) within GIS framework was developed by introducing new concepts, which consist of the extended area containing the study area and the additional site visit for acquiring surface geo-knowledge data. The GIS-based GTIS was built for Gyeongju area, which has records of abundant historical seismic hazards reflecting the high potential of future earthquakes. At the study area, Gyeongju, intensive site investigations and pre-existing geotechnical data collections were performed and the site visits were additionally carried out for assessing geotechnical characteristics and shear wave velocity ($V_S$) representing dynamic property. Within the GTIS for Gyeongju area, the spatially distributed geotechnical layers and $V_S$ in the entire study area were reliably predicted from the site investigation data using the geostatistical kriging method. Based on the spatial geotechnical layers and $V_S$ predicted within the GTIS, a seismic zoning map on site period ($T_G$) from which the site-specific seismic responses according to the site effects can be estimated was created across the study area of Gyeongju. The spatial $T_G$ map at Gyeongju indicated seismic vulnerability of two- to five-storied buildings. In this study, the seismic zonation based on $T_G$ within the GIS-based GTIS was presented as regional efficient strategy for seismic hazard prediction and mitigation.

• Journal of Korea Water Resources Association
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• v.35 no.1
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• pp.1-12
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• 2002

This study was intended to investigate the rainfall-runoff relationship with spatially distributed rainfall data, and then, to analyze and quantify the uncertainty induced by spatially averaging rainfall data. For constructing spatially distributed rainfall data, several historical rainfall events were extended spatially by simple kriging method based on the semivariogram as a function of the relative distance. Runoff was computed by two models; one was the modified Clark model with spatially distributed rainfall data and the other was the conventional Clark model with spatially averaged rainfall data. Rainfall errors and discharge errors occurred through this process were defined and analyzed with respect to various rain-gage network densities. The following conclusions were derived as the results of this work; 1) The conventional Clark parameters could be appropriate for translating spatially distributed rainfall data. 2) The parameters estimated by the modified Clark model are more stable than those of the conventional Clark model. 3) Rainfall and discharge errors are shown to be reduced exponentially as the density of rain-gage network is increased. 4) It was found that discharge errors were affected largely by rainfall errors as the rain-gage network density was small.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.2
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• pp.216-232
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• 2016

In present, the Seoul City is undergoing traffic congestion problems caused by rapid urbanization and population growth. Thus the City government has reorganized the mass transportation system since 2004 and the subway has become a very important means for public transit. Since the subway system is typically a closed environment, the indoor air quality issues have often raised by the public. Especially since a huge amount of PM (particulate matter) is emitted from ground tunnels passing through the subway train, it is now necessary to assess the characteristics and behaviors of fine PM inside the tunnel. In this study, the concentration patterns of $PM_1$, $PM_{2.5}$, and $PM_{10}$ in the Seoul subway line-2 were analyzed by real-time measurement during winter (Jan 13, 2015) and summer (Aug 7, 2015). The line-2 consisting of 51 stations is the most busy circular line in Seoul having the railway of 60.2 km length. The the one-day average $PM_{10}$ concentrations were $148{\mu}g/m^3$ in winter and $66.3{\mu}g/m^3$ in summer and $PM_{2.5}$ concentrations were $118{\mu}g/m^3$ and $58.5{\mu}g/m^3$, respectively. The $PM_{2.5}/PM_{10}$ ratio in the underground tunnel was lower than the outdoor ratio and also the ratio in summer is higher than in winter. Further the study examined structural types of underground subsections to explain the patterns of elevated PM concentrations in the line-2. The subsections showing high PM concentration have longer track, shorter curvature radius, and farther from the outdoor stations. We also estimated the outdoor PM concentrations near each station by a spatial statistical analysis using the $PM_{10}$ data obtained from the 40 Seoul Monitoring Sites, and further we calculated $PM_{2.5}/PM_{10}$ and $PM_1/PM_{10}$ mass ratios near the outdoor subway stations by using our observed outdoor $PM_1$, $PM_{2.5}$, and $PM_{10}$ data. Finally, we could develop pollution maps for outdoor $PM_1$ and $PM_{2.5}$ near the line-2 by using the kriging method in spatial analysis. This methodology may help to utilize existing $PM_{10}$ database when managing and control fine particle problems in Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.434-439
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• 2019

Recently, interest in urban temperature change and ground surface temperature change has been increasing due to weather phenomenon due to global warming, heat island phenomenon caused by urbanization in urban areas. In Korea, weather data such as temperature and precipitation have been collected since 1904. In recent years, there are 96 ASOS stations and 494 AWS weather observation stations. However, in the case of terrestrial networks, terrestrial meteorological data except measurement points are predicted through interpolation because they provide point data for each installation point. In this study, to improve the resolution of ground surface temperature measurement, the surface temperature using satellite image was calculated and its applicability was analyzed. For this purpose, the satellite images of Landsat 8 OLI TIRS were obtained for Seoul Metropolitan City by seasons and transformed to surface temperature by applying NASA equation to the thermal bands. The ground measurement data was based on the temperature data measured by AWS. Since the AWS temperature data is station based point data, interpolation is performed by Kriging interpolation method for comparison with Landsat image. As a result of comparing the satellite image base surface temperature with the AWS temperature data, the temperature difference according to the season was calculated as fall, winter, summer, based on the RMSE value, Spring, in order of applicability of Landsat satellite image. The use of that attribute and AWS support starts at $2.11^{\circ}C$ and RMSE ${\pm}3.84^{\circ}C$, which reflects information from the extended NASA.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.187-196
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• 2021

The paper deals with comparative study for characteristics of approximation of design space according to various approximate models and sensitivity analysis using orthogonal array experiments in structure design of active type DSF which was developed for float-over installation of offshore plant. This study aims to propose the orthogonal array experiments based design methodology which is able to efficiently explore an optimum design case and to generate the accurate approximate model. Thickness sizes of main structure member were applied to the design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experiment. Best design case was also identified to improve the structure design with weight minimization. From the orthogonal array experiment results, various approximate models such as response surface model, Kriging model, Chebyshev orthogonal polynomial model, and radial basis function based neural network model were generated. The experiment results from orthogonal array method were validated by the approximate modeling results. It was found that the radial basis function based neural network model among the approximate models was able to approximate the design space of the active type DSF with the highest accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.357-367
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• 2018

Recent earthquakes in Gyeongju and Pohang have raised interest in liquefaction in South Korea. Liquefaction, which is a phenomenon that excessive pore pressure is generated and the shear strength of soil is decreased by repeated loads such as earthquakes, causes severe problems such as ground subsidence and overturning of structures. Therefore, it is necessary to identify and prepare for the possibility of liquefaction in advance. In general, the possibility of liquefaction is quantitatively assessed using the Liquefaction Potential Index (LPI), but it takes a lot of time and effort for performing site response analysis which is essential for the liquefaction evaluation. In this study, a simple method to evaluate the liquefaction potential without executing the site response analysis in a downtown area with a lot of borehole data was proposed. In this simple method, the correlation between the average shear wave velocity of the target location ground and the LPI divided by thickness of liquefiable layer was established. And the applicable correlation equation for various rock outcrop accelerations were derived. Using the 104 boreholes information in Seoul, the correlation equation between LPI and the shear wave velocity (ground water level: 0m, 1m, 2m, 3m) is obtained and the possibility of liquefaction occurrence in Seoul and Gyeongju is evaluated. The applicability of the proposed simple method was verified by comparing the LPI values calculated from the correlation equation and the LPI values derived using the existing site response analysis. Finally, the distribution map of LPI calculated from the correlation was drawn using Kriging, a geostatistical technique.

• Economic and Environmental Geology
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• v.38 no.4 s.173
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• pp.435-450
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• 2005

The purpose of the study is to analyze the hydrogeochemical characteristics by multivariate statistical method, to interpret the hydrogeochemical processes for the new variables calculated from principal components analysis (PCA), and to infer the groundwater flow and circulation mechanism by applying the geostatistical methods for each element and principal component. Chloride and nitrate are the most influencing components for groundwater quality, and the contents of $NO_3$ increased by the input of agricultural activities show the largest variation. The results of PCA, a multivariate statistical method, show that the first three principal components explain $73.9\%$ of the total variance. PC1 indicates the increase of dissolved ions, PC2 is related with the dissolution of carbonate minerals and nitrate contamination, and PC3 shows the effect of cation exchange process and silicate mineral dissolution. From the results of experimental semivariogram, the components of groundwater are divided into two groups: one group includes electrical conductivity (EC), Cl, Na, and $NO_3$, and the other includes $HCO_3,\;SiO_2,$ Ca, and Sr. The results for spatial distribution of groundwater components showed that EC, Cl, and Na increased with approaching the coastal line and nitrate has close relationship with the presence of agricultural land. These components are also correlated with the topographic features reflecting the groundwater recharge effect. The kriging analysis by using principal components shows that PC 1 has the different spatial distribution of Cl, Na, and EC, possibly due to the influence of pH, Ca, Sr, and $HCO_3$ for PC1. It was considered that the linear anomaly zone of PC2 in western area was caused by the dissolution of carbonate mineral. Consequently, the application of multivariate and geostatistical methods for groundwater in the study area is very useful for determining the quantitative analysis of water quality data and the characteristics of spatial distribution.