• The Journal of Engineering Geology
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• v.29 no.3
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• pp.265-277
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• 2019

Changes in the amount, intensity, frequency, and type of precipitation, in conjunction with global warming and climate change, critically impact groundwater recharge and associated groundwater level fluctuations. Monthly gravity levels by the Gravity Recovery and Climate Experiment (GRACE) are acquired to monitor total water storage changes at regional and global scales. However, there are inherent difficulties in quantitatively relating the GRACE observations to groundwater level data due to the difficulties in spatially representing groundwater levels. Here three local interpolation methods (kriging, inverse distance weighted, and natural neighbor) were implemented to estimate the areal distribution of groundwater recharge changes in South Korea during the 2002-2016 period. The interpolated monthly groundwater recharge changes are compared with the GRACE-derived groundwater storage changes. There is a weak decrease in the groundwater recharge changes over time in both the GRACE observations and groundwater measurements, with the rate of groundwater recharge change exhibiting mean and median values of -0.01 and -0.02 cm/month, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1831-1836
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• 2014

Probabilistic liquefaction hazard map is now widely needed for engineering practice. Based on the Liquefaction Potential Index (LPI) calculated from liquefied and non-liquefied cases, we attempted to estimate probabilities of liquefaction induced ground failures using logistic regression. We then applied this approach for the regional area. LPIs were calculated based on 273 Standard Penetration Tests in the floodplains in the St. Louis area, USA and then interpolated using cokriging with the covariable of peak ground acceleration. Our result shows that some areas of $LPI{\geq}5$, due to soft soil layers and shallow groundwater table, appear probabilities of ground $failure{\geq}0.5$.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.4
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• pp.771-785
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• 2014

As marine desertification and chlorosis in Korean coast have been intensified over time, Korean government is promoting marine afforestation projects. However, marine afforestation location is mainly decided by administrative convenience. Also, there is limited literature on location suitability about the marine afforestation. This study aims to assess location suitability of marine afforestation considering 3 significant criteria: ecological, submarine topographical, and human-social environment. Jeju, the study area of this study, first observed chlorosis in Korean coast at the small fishery town in Seogwipo. Jeju is currently suffering from chlorosis all around the island. Habitat Evaluation Procedure (HEP), 3D kriging, Analytic Hierarchy Process (AHP) is applied as analysis methods. Especially, 3D kriging is utilized for modeling 3D ocean space reflecting ocean environment appropriately. The result shows that Jocheon coast has better location suitability than Seogwipo Pyoseon coast. Jocheon coast has the maximum 61% suitability as the habitat of Ecklonia cava Kjellman, and is highly evaluated in other criteria. The results of this study are expected to find optimal marine afforestation location, and to contribute to the restoration of the Jeju coastal ecosystem and the revitalization of Jeju fishing village societies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.393-405
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• 2020

Various geotechnical information is required to evaluate the stability of the ground and a foundation once liquefaction occurs due to earthquakes, such as the soil strength and groundwater level. The results of the Standard Penetration Test (SPT) conducted in Korea are registered in the National Geotechnical Information Portal System. If geotechnical information for a non-drilled area is needed, geostatistics can be applied. This paper is about the feasibility of obtaining ground information by the Empirical Bayesian Kriging (EBK) method and the Inverse Distance Weighting Method (IDWM). Esri's ArcGIS Pro program was used to estimate these techniques. The soil strength parameter of the drilling area and the level of groundwater obtained from the standard penetration test were cross-validated with the results of the analysis technique. In addition, Multichannel Analysis of Surface Waves (MASW) was conducted to verify the techniques used in the analysis. The Buk-gu area of Pohang was divided into 1.0 km×1.0 km and 110 zones. The cross-validation for the SPT N value and groundwater level through EBK and IDWM showed that both techniques were suitable. MASW presented an approximate section area, making it difficult to clearly grasp the distribution pattern and groundwater level of the SPT N value.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Journal of Korea Water Resources Association
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• v.50 no.7
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• pp.455-465
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• 2017

Understanding of the two-dimensional velocity field is crucial in terms of analyzing various hydrodynamic and fluvial processes in the riverine environments. Until recently, many numerical models have played major roles of providing such velocity field instead of in-situ flow measurements, because there were limitations in instruments and methodologies suitable for efficiently measuring in the broad range of river reaches. In the last decades, however, the advent of modernized instrumentations started to revolutionize the flow measurements. Among others, acoustic Doppler current profilers (ADCPs) became very promising especially for accurately assessing streamflow discharge, and they are also able to provide the detailed velocity field very efficiently. Thus it became possible to capture the velocity field only with field observations. Since most of ADCPs measurements have been mostly conducted in the cross-sectional lines despite their capabilities, it is still required to apply appropriate interpolation methods to obtain dense velocity field as likely as results from numerical simulations. However, anisotropic nature of the meandering river channel could have brought in the difficulties for applying simple spatial interpolation methods for handling dynamic flow velocity vector, since the flow direction continuously changes over the curvature of the channel shape. Without considering anisotropic characteristics in terms of the meandering, therefore, conventional interpolation methods such as IDW and Kriging possibly lead to erroneous results, when they dealt with velocity vectors in the meandering channel. Based on the consecutive ADCP cross-sectional measurements in the meandering river channel. For this purpose, the geographic coordinate with the measured ADCP velocity was converted from the conventional Cartesian coordinate (x, y) to a curvilinear coordinate (s, n). The results from application of A-VIM showed significant improvement in accuracy as much as 41.5% in RMSE.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.584-584
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• 2015

최근 기후변화로 인한 국지성 집중호우와 태풍 등으로 홍수피해가 급증하고 있음에 따라 침수지역에 대한 공간적인 분석과 사전 예측으로 피해를 최소화하려는 노력이 필요하다. 따라서 본 연구에서는 소유역 별 평균화된 매개변수로 홍수량을 산정하는 집중형 모형이 아닌 분포형 모형을 적용하여 남강댐 유역의 유출량 산정 및 침수예측을 분석하였다. 분포형 모형은 격자체계를 기반으로 유역에 각 격자별 공간적 특성이 반영된 매개변수를 적용하므로 유역의 특성을 효과적으로 반영하므로 집중형 모형보다 정확한 해석이 가능하다. DEM, 토양도, 토지피복도 등의 격자크기 $240{\times}240$의 지형공간 자료를 ArcGIS를 이용하여 남강댐유역의 Flow direction, 경사도, 하도경사, 불투수율, 유효공극률, 조도계수, 토양심도, 수리전도도, 토양흡인수두 등의 수문매개변수를 추출하였다. 강우 자료의 경우 티센(Thiessen)법에 의해 선정된 남강댐유역 주변의 장수, 거창, 진주, 합천, 산청, 남원 강우관측소의 100년빈도 확률강우량 산정하여 24시간 확률강우를 3분위 Huff 분포시킨 후 강우의 공간적 통계특성을 반영하는 크리깅(Kriging)기법으로 적용하여 강우보간을 실시하였다. 침수예측을 위해 $Vflo^{TM}$모형을 이용해 48시간의 강우모의시간 홍수수문곡선 유도 및 홍수량 산정하였으며, 시간에 따른 침수 시뮬레이션하여 침수예측도를 작성하였다. 작성 시 침수심의 정도에 따라 5개의 구간으로 분류해 침수위험지역을 확인 할 수 있도록 도식화하였다. 본 연구에서는 남강댐유역의 침수위험지역을 개략적으로 예측할 수 있었으며, 추후 연구에서는 보다 조밀한 격자크기와 강우를 이용하여 분석한다면 향후 피난 정보 제공과 홍수재해지도 작성, 홍수방지 시설물 건설 또는 홍수보험계획 등에 응용이 될 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.681-690
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• 2017

This study estimated the daily maximum snow depth using the Artificial Neural Network (ANN) model in Korean Peninsula. First, the optimal ANN model structure was determined through the trial-and-error approach. As a result, daily precipitation, daily mean temperature, and daily minimum temperature were chosen as the input data of the ANN. The number of hidden layer was set to 1 and the number of nodes in the hidden layer was set to 10. In case of using the observed value as the input data of the ANN model, the cross validation correlation coefficient was 0.87, which is higher than that of the case in which the daily maximum snow depth was spatially interpolated using the Ordinary Kriging method (0.40). In order to investigate the performance of the ANN model for estimating the daily maximum snow depth of the ungauged area, the input data of the ANN model was spatially interpolated using Ordinary Kriging. In this case, the correlation coefficient of 0.49 was obtained. The performance of the ANN model in mountainous areas above 200m above sea level was found to be somewhat lower than that in the rest of the study area. This result of this study implies that the ANN model can be used effectively for the accurate and immediate estimation of the maximum snow depth over the whole country.

• Geophysics and Geophysical Exploration
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• v.15 no.1
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• pp.16-22
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• 2012

In this study, we setup a ground-based scatterometer using an antenna of which the center frequency is 9.5 GHz (X-band), and measured radar backscatterings from air/ice and ice/water interfaces to extract ice thickness. Both of air/ice and ice/water interfaces make strong radar backscatterings and so we can clearly identify two peaks in measured data by scatterometer. By using the distance of two peaks and refractive index of ice, we confirmed that it is possible to measure ice thickness. Field survey was performed at the downstream of Jiam River flowing into Chuncheon Lake. We measured radar backscattering from river ice along a survey path and extracted ice thickness. The ice thickness map of the downstream of Jiam River was produced by using kriging which is one of well known interpolation methods. The ice thickness was about 50 cm along the mainstream while ice was thin as 30 ~ 40 cm at a fast-flowing meander. Ice thickness was particularly thinner at some locations than that of surrounding areas even in the mainstream region of constant flow. This was because of impurities in ice or artificially formed refrozen holes after fishing. We expect that this study helps to expand utilization field of X-band SAR and airborne scatterometer system.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.175-184
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• 2017

Recently, PM10 is becoming a main issue in Korea because it causes a variety of diseases, such as respiratory and ophthalmologic diseases. This research studied to spatial information application cases for evaluating the feasibility of the location for PM10 observation stations utilizing Geogrphic Information System(GIS) spatial analysis. The spatial Information application cases for optimal location assessment were investigated to properly manage PM10 observation stations which are closely related with public spatial data and health care. There are 31 PM10 observation stations in Seoul city and the observed PM10 data at these stations were utilized to understand the overall assessment of PM10 stations to properly manage using interpolation methods. The estimated PM10 using Inverse Distance Weighted(IDW) and Kriging techniques and the map of PM10 concentrations of monitoring stations in Seoul city were compared with public spatial data such as precipitation, floating population, elementary school location. On the basis of yearly, seasonal and daily PM10 concentrations were used to evaluate the feasibility analysis and the location of current PM10 monitoring stations. The estimated PM10 concentrations were compared with floating population and calculated 2015 PM10 distribution data using zonal statistical methods. The national spatial data could be used to analyze the PM10 pollution distribution and additional determination of PM10 monitoring sites. It is further suggested that the spatial evaluation of national spatial data can be used to determine new location of PM10 monitoring stations.