• Journal of the Korean earth science society
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• v.35 no.2
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• pp.115-130
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• 2014

The growing possibility of the disaster due to severe weather calls for disaster prevention and water management measures in South Korea. In order to prevent a localized heavy rain from occurring, the rainfall must be observed and predicted quantitatively. In this study, we developed an adjustment algorithm to estimate the radar precipitation applying to the local gauge correction (LGC) method which uses geostatistical effective radius of errors of the radar precipitation. The effective radius was determined from the errors of radar rainfall using geostatistical method, and we adjusted radar precipitation for four heavy rainfall events based on the LGC method. Errors were decreased by about 40% and 60% in adjusted hourly rainfall accumulation and adjusted total rainfall accumulation for four heavy rainfall events, respectively. To estimate radar precipitation for localized heavy rain events in summer, therefore, we believe that it was appropriate for this study to use an adjustment algorithm, developed herein.

• Journal of the Korean Society of Groundwater Environment
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• v.7 no.3
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• pp.125-132
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• 2000

pH, EC and TDS are basic components in the investigation of groundwater quality, and are very important to the preliminary assessment of groundwater quality. These three chemical components investigated at the Youngsan and Sumjin river basins in 1998 suggest that the groundwater quality is generally good in these basins. Linear regression analysis shows that TDS versus EC has an linear correlation, but EC versus pH, and TDS versus pH have nearly no correlation. The relation of TDS and EC is 1.0 mg/1=1.52 $mu\textrm{S}$/cm, and it is the quality of natural water. In geostatistical analysis. three kinds of data are stationary random functions and they have exponential variograms. According to the isopleth maps of the groundwater quality, the groundwater quality of the Youngsan river basin is more contaminated than that of the Sumjin river basin. The isopleth maps of TDS and EC show very similar patterns because of the strong correlation between TDS and EC. The minimum and maximum values of the groundwater quality data are not reflected on the isopleth maps because kriging produces smooth distributions with minimum estimation variances.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.379-394
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• 2021

Air pollution data in South Korea is provided on a real-time basis by Air Korea stations since 2005. Previous studies have shown the feasibility of gridding air pollution data, but they were confined to a few cities. This paper examines the creation of nationwide gridded maps for PM10 concentration using 333 Air Korea stations with variogram optimization and ordinary kriging. The accuracy of the spatial interpolation was evaluated by various sampling schemes to avoid a too dense or too sparse distribution of the validation points. Using the 114,745 matchups, a four-round blind test was conducted by extracting random validation points for every 365 days in 2019. The overall accuracy was stably high with the MAE of 5.697 ㎍/m³ and the CC of 0.947. Approximately 1,500 cases for high PM10 concentration also showed a result with the MAE of about 12 ㎍/m³ and the CC over 0.87, which means that the proposed method was effective and applicable to various situations. The gridded maps for daily PM10 concentration at the resolution of 0.05° also showed a reasonable spatial distribution, which can be used as an input variable for a gridded prediction of tomorrow's PM10 concentration.

• 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.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.271-280
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• 2005

Kriging methods as traditional spatial data analysis methods and geographical weighted regression models as statistical analysis methods are compared. In this paper, we apply data from the Ministry of Environment to spatial analysis for practical study. We compare these methods to performance with monthly carbon monoxide observations taken at 116 measuring area of air pollution in 1999.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.459-473
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• 2007

In this paper, we suggest a spatial regression model to predict AADT. Although Euclidian distances between one monitoring site and its neighboring sites were usually used in the many analysis, we consider the shortest travel path between monitoring sites to predict AADT for unmonitoring site using spatial regression model. We used universal Kriging method for prediction and found that the overall predictive capability of the spatial regression model based on shortest travel path is better than that of the model based on multiple regression by cross validation.

• The Journal of Engineering Geology
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• v.16 no.4 s.50
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• pp.393-402
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• 2006

The contamination characteristics of BTEX and TPH components in silty soils with the oil leakage event from point source were studied. The over ratios of three soil pollution standard for TPH component were $1.5{\sim}1.7$ times higher than that of BTEX component. The mean and maximum values of BTEX and TPH components with sample points were B-zone > A-zone > C-zone, and the highest concentrations were measured at $1{\sim}2m$ depth below surface. BTEX and TPH components were increased with linear distance in zone within 120 m and 80 m from point source. For the zone more than 120 m, BTEX and TPH concentrations were under soil pollution standard. The cutoff values of indicator kriging using BTEX and TPH components were defined as confirmative limit, warn- ing limit and counterplan limit. The variograms of indicator-transformed data were selected linear model. The contamination ranges of BTEX and TPH components using confirmative limit and warning limit were estimated similar, but the contamination range of those using counterplan limit was much reduced. The maximum contamination probabilities were estimated by probability maps usinB confirmative limit, warning limit and counterplan limit. The maximum contamination probabilities with three soil pollution standard were estimated 26%, 26% and 13% for BTEX component, and 44%, 38% and 26% for TPH component.

• Journal of the Korean Society of Groundwater Environment
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• v.2 no.2
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• pp.58-63
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• 1995

Cokriging was applied for the estimation of the water levels of the basal leachate and the surrounding groundwater at the Nanjido waste landfill area. When the groundwater level is estimated at the high relief area, it makes a good result to use the data of groundwater level and elevation simultaneously because groundwater level is correlated with topography. This study determined the best semivariogram model of 87 groundwater levels and 144 elevations through cross validation test, and produced the contour maps of groundwater levels using ordinary kriging and universal kiging. Two contour maps don't make big difference at the waste site because this area has a large number of groundwater level data. However, they show big difference at the upper left part of the study area because this area has high relief and a small number of sample data. Their difference is also found at the south area near the Han river. When the topography is considered for the both areas, the contour map of cokriging is thought to be closer to the real groundwater distribution than that of kriging.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.413-413
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• 2011

강우의 공간분포에 대한 신뢰성 있는 추정은 수자원 해석 및 설계에 있어서 필수적인 요소이다. 강우장의 공간변동성에 대한 고해상도 추정은 홍수, 특히 돌발홍수의 원인이 되는 국지성 호우의 확인 및 분석에 있어서 중요하다. 또한 강우의 공간 변동성에 대한 고려는 면적평균강우량 추정의 정확도를 향상시키는데 있어서 중요하며, 강우-유출모델의 모의결과에 대한 신뢰도를 향상시키는데 큰 영향을 미친다. 최근 공간자료에 대한 공간분포예측에 있어서 공간상관성을 고려할 수 있는 공간통계학적 기법의 적용이 증가하고 있으며, 이러한 공간통계학적 기법의 적용에 있어서 신뢰성 있는 모델 매개변수의 추정 및 불확실성 평가는 공간분포 예측결과에 대한 신뢰성을 향상시키는데 중요한 역할을 한다. 외국의 경우 공간분포예측 및 모의, 매개변수의 불확실성 평가 등과 관련하여 활발한 연구가 이루어지고 있는 반면 국내 수자원 분야에서는 아직까지 활발한 연구가 이루어지고 있지 않은 실정이다. 따라서 본 연구에서는 계층구조로 구성된 가우시안 공간선형혼합모델을 적용하여 확률강우량의 공간분포를 추정함에 있어서 모델 매개변수에 대한 추정기법을 비교하였으며, 매개변수 추정기법으로서 경험베리오그램에 대한 곡선적합기법인 보통최소제곱법 및 가중최소제곱법, 우도함수를 기반으로 하는 최우도법 및 REML과 같은 기존의 매개변수 추정기법들과 최근 공간통계학 분야에서 적용이 증가하고 있는 Bayesian 기법을 비교하였다. 이로부터 매개변수 추정기법 간의 매개변수 추정치에 대한 정량적 비교결과를 제시하였으며, Bayesian 기법의 적용을 통해 매개변수에 대한 불확실성 추정결과를 제시하였다. 이러한 결과들은 확률강우량의 공간분포 추정에 있어서 공간예측모델의 매개변수 추정 및 예측에 대한 신뢰성을 향상시킬 수 있는 기초자료로 활용될 수 있을 것이다.

• 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.