• Title/Summary/Keyword: geostatistical analysis

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Analysis of Spatial Variability for Particle Size Distribution of Field Soils -I. Variogram (토양(土壤)의 입경분포(粒徑分布)에 대(對)한 공간변이성(空間變異性) 분석(分析) -I. Variogram)

  • Park, Chang-Seo;Kim, Jai-Joung;Cho, Seong-Jin
    • Korean Journal of Soil Science and Fertilizer
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    • v.17 no.3
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    • pp.212-217
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    • 1984
  • Spatial variabilities of particle size distribution of 96 samples from Hwadong SiCL and Jungdong Sl were studied by using geostatistical concepts. The measurement was made at the nodes of the regular grid consisting of 12 rows and 8 columns. Sample spacing within rows and columns was 3 and 2 meters, respectively. The results are summarized as follows. 1. Variograms of Hwadong SiCL were fitted for the linear model and those of Jungdong SL for the spherical model. 2. Variograms of properties for Hwadong and clay for Jungdong showed the pure nugget effect. Those of silt and clay for Jungdong, however, appeared the nugget effect. 3. The minimum number of samples necessary to reproduce results similar to the true mean of the 96 measured values was approximately estimated. The minimum sample sizes of silt, clay, and sand in Hwadong SiCL were 27, 13, and 6, respectively. And the minimum sample size of clay in Jungdong SL was 17. 4. The approximate number of samples required to detect the difference of 5% of the true mean with 0.95 confidence level was estimated. The resulting number of samples for silt and sand in Jungdong was 14, and 26, respectively.

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Spatial Distribution Modeling of Daily Rainfall Using Co-Kriging Method (Co-kriging 기법을 이용한 일강우량 공간분포 모델링)

  • Hwang Sye-Woon;Park Seung-Woo;Jang Min-Won;Cho Young-Kyoung
    • Journal of Korea Water Resources Association
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    • v.39 no.8 s.169
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    • pp.669-676
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    • 2006
  • Hydrological factors, especially the spatial distribution of interpretation on precipitation is often topic of interest in studying of water resource. The popular methods such as Thiessen method, inverse distance method, and isohyetal method are limited in calculating the spatial continuity and geographical characteristics. This study was intended to overcome those limitations with improved method that will yield higher accuracy. The monthly and yearly precipitation data were produced and compared with the observed daily precipitation to find correlation between them. They were then used as secondary variables in Co-kriging method, and the result was compared with the outcome of existing methods like inverse distance method and kriging method. The comparison of the data showed that the daily precipitation had high correlation with corresponding year's average monthly amounts of precipitation and the observed average monthly amounts of precipitation. Then the result from the application of these data for a Co-kriging method confirmed increased accuracy in the modeling of spatial distribution of precipitation, thus indirectly reducing inconsistency of the spatial distribution of hydrological factors other than precipitation.

Kriging Analysis for Spatio-temporal Variations of Ground Level Ozone Concentration

  • Gorai, Amit Kumar;Jain, Kumar Gourav;Shaw, Neha;Tuluri, Francis;Tchounwou, Paul B.
    • Asian Journal of Atmospheric Environment
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    • v.9 no.4
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    • pp.247-258
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    • 2015
  • Exposure of high concentration of ground-level ozone (GLO) can trigger a variety of health problems including chest pain, coughing, throat irritation, asthma, bronchitis and congestion. There are substantial human and animal toxicological data that support health effects associated with exposure to ozone and associations have been observed with a wide range of outcomes in epidemiological studies. The aim of the present study is to estimate the spatial distributions of GLO using geostatistical method (ordinary kriging) for assessing the exposure level of ozone in the eastern part of Texas, U.S.A. GLO data were obtained from 63 U.S. EPA's monitoring stations distributed in the region of study during the period January, 2012 to December, 2012. The descriptive statistics indicate that the spatial monthly mean of daily maximum 8 hour ozone concentrations ranged from 30.33 ppb (in January) to 48.05 (in June). The monthly mean of daily maximum 8 hour ozone concentrations was relatively low during the winter months (December, January, and February) and the higher values observed during the summer months (April, May, and June). The higher level of spatial variations observed in the months of July (Standard Deviation: 10.33) and August (Standard Deviation: 10.02). This indicates the existence of regional variations in climatic conditions in the study area. The range of the semivariogram models varied from 0.372 (in November) to 15.59 (in April). The value of the range represents the spatial patterns of ozone concentrations. Kriging maps revealed that the spatial patterns of ozone concentration were not uniform in each month. This may be due to uneven fluctuation in the local climatic conditions from one region to another. Thus, the formation and dispersion processes of ozone also change unevenly from one region to another. The ozone maps clearly indicate that the concentration values found maximum in the north-east region of the study area in most of the months. Part of the coastal area also showed maximum concentrations during the months of October, November, December, and January.

Analysis of Spatial Variability for Infiltration Rate of Field Soil -I. Variogram (토양(土壤)중 물의 침투속도(浸透速度)의 공간변이성(空間變異性) 분석(分析) -I. Variogram)

  • Park, Chang-Seo;Kim, Jai-Joung;Cho, Seong-Jin
    • Korean Journal of Soil Science and Fertilizer
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    • v.16 no.4
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    • pp.305-310
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    • 1983
  • Spatial variability of infiltration rates of 96 samples from Hwadong SiCL was studied by using geostatistical concepts. The measurement was made at the nodes of the regular grid consisting of 12 rows and 8 columns. Sample spacing within rows and columns was 3 and 2 meters, respectively. This study illustrated the use of variogram as a tool to identify the degree of dependency of the infiltration rate on the distance between pairs of measurements and how to take advantage of this dependency. Fractile diagram showed that the distribution of observation was approximately normal. The range of the variogram was about 7.4 meters. The minimum number of samples necessary to reproduce the results similar to the 96 measured values was 8 to 10. Coefficients of theoretical variogram function for computing kriged values and kriged varionces of nuogget effect, slope, and range were 0.444 cm/day, 0.003 cm/day, and 7.4 m, respectively.

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Quantifying the Spatial Heterogeneity of the Land Surface Parameters at the Two Contrasting KoFlux Sites by Semivariogram (세미베리오그램을 이용한 KoFlux 광릉(산림) 및 해남(농경지) 관측지 지면모수의 공간 비균질성 정량화)

  • Moon, Sang-Ki;Ryu, Young-Ryel;Lee, Dong-Ho;Kim, Joon;Lim, Jong-Hwan
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.9 no.2
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    • pp.140-148
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    • 2007
  • The remote sensing observations of land surface properties are inevitably influenced by the landscape heterogeneity. In this paper, we introduce a geostatistical technique to provide a quantitative interpretation of landscape heterogeneity in terms of key land surface parameters. The study areas consist of the two KoFlux sites: (1) the Gwangneung site, covered with temperate mixed forests on a complex terrain, and (2) the Haenam site with mixed croplands on a relatively flat terrain. The semivariogram and fractal analyses were performed for both sites to characterize the spatial heterogeneity of two radiation parameters, i.e., land surface temperature (LST) and albedo. These parameters are the main factors affecting the reflected longwave and shortwave radiation components from the two study sites. We derived them from the high-resolution Landsat ETM+ satellite images collected on 23 Sep. 2001 and 14 Feb. 2002. The results of our analysis show that the characteristic scales of albedo was >1 km at the Gwangneung site and approximately 0.3 km at the Haenam site. For LST, the scale of heterogeneity was also >1 km at the Gwangneung site and >0.6 to 1.0 km at the Haenam site. At both sites, there was little change in the characteristic scales of the two parameters between the two different seasons.

Assessment of Liquefaction Potential Using Correlation between Shear Wave Velocity and Normalized LPI on Urban Areas of Seoul and Gyeongju (정규화LPI와 전단파 속도의 상관관계를 활용한 서울과 경주 지역 액상화 위험도 평가)

  • Song, Young Woo;Chung, Choong Ki;Park, Ka Hyun;Kim, Min Gi
    • 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.