• Journal of the Korean Society of Groundwater Environment
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• v.4 no.1
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• pp.14-19
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• 1997

Laboratory experiments were conducted to study the effects of pH and salt level on the soil sorption equilibrium of pentachlorophenol (PCP) which is hydrophobic and ionogenic. Experimental results indicated that the sorption equilibrium constant (Kp) of PCP increased with decreasing pH. A quantitative sorption model involving linear isotherms was estabilished to predict the pH effect on the PCP sorption equilibrium over the pH range from 3 to 8. The model prediction was in good agreement with the experimental data. Also, the Kp increased with salt concentration over the entire pH range. At added salt levels less than 0.1M, increase in Kp was larger than when the added levels were higher than 0.1M. Salt might increase the PCP sorption by inducing 'salting out-effect' or by forming deprotonated PCP-cation ion pairs such as PCP$\^$-/K$\^$＋/. Taking the pH range (5-8) and the salt content (up to 50 g/L) in the groundwater of Metropolitan landfill sites into consideration, the results indicated that the retardation factor of PCP in this area might range from 3 to 550 depending upon pH and salt content.

• Journal of the Korean Geotechnical Society
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• v.33 no.7
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• pp.55-64
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• 2017

A geological repository has been considered one of the most adequate options for the disposal of high-level radioactive waste. A geological repository will be constructed in a host rock at a depth of 500~1,000 meters below the ground surface. The geological repository system consists of a disposal canister with packed spent fuel, buffer material, backfill material, and intact rock. The buffer is very important to assure the disposal safety of high-level radioactive waste. It can restrain the release of radionuclide and protect the canister from the inflow of groundwater. High temperature in a disposal canister is released into the surrounding buffer material, and thus the thermal transfer behavior of the buffer material is very important to analyze the entire disposal safety. Therefore, this paper presents a thermal conductivity prediction model for the Kyungju compacted bentonite buffer material which is the only bentonite produced in Korea. Thermal conductivity of Kyungju bentonite was measured using a hot wire method according to various water contents and dry densities. With 39 data obtained by the hot wire method, a regression model to predict the thermal conductivity of Kyungju bentonite was suggested.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.581-593
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• 2006

Various geological thematic maps have been generated by interpolating sparsely sampled ground survey data and geostatistical kriging that can consider spatial correlation between neighboring data has widely been used. This paper applies multi-variate geostatistical algorithms to integrate secondary information with sparsely sampled ground survey data for geological thematic mapping. Simple kriging with local means and kriging with an external drift are applied among several multi-variate geostatistical algorithms. Two case studies for spatial mapping of groundwater level and grain size have been carried out to illustrate the effectiveness of multi-variate geostatistical algorithms. A digital elevation model and IKONOS remote sensing imagery were used as secondary information in two case studies. Two multi-variate geostatistical algorithms, which can account for both spatial correlation of neighboring data and secondary data, showed smaller prediction errors and more local variations than those of ordinary kriging and linear regression. The benefit of applying the multi-variate geostatistical algorithms, however, depends on sampling density, magnitudes of correlation between primary and secondary data, and spatial correlation of primary data. As a result, the experiment for spatial mapping of grain size in which the effects of those factors were dominant showed that the effect of using the secondary data was relatively small than the experiment for spatial mapping of groundwater level.

• Korean Journal of Soil Science and Fertilizer
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• v.41 no.3
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• pp.228-234
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• 2008

The purpose of this study was to predict environmental distribution of anthracene, benzene, benzo[a]pyrene, 1-methylphenanthrene and phenanthrene in a four phase biopile system - air, water, soil and non aqueous phase liquid (NAPL) phase using level I fugacity model. Soil samples used for this study were collected from three sites in the United Kingdom which were historically contaminated with petroleum hydrocarbons. The level I fugacities (f) for the five contaminants were markedly different, however, the fugacities of each contaminant in three soil samples did not show significant difference. NAPL and soil were the dominant phases for all five contaminants. Results of this study indicated that difference in percentage of organic carbon strongly influenced the partitioning behavior of the cntaminants. The presence of benzene calls for an urgent need for risk-based management of air and water phase. Whereas insignificant amount of chemicals leached in the water phase for other organic contaminants showing greatly reduced potential of groundwater contamination. Furthermore, this study helped us to confirm the association of risk critical contaminants with the residual saturation in treated soils. They also can be used to emphasize the importance of accounting for the partitioning behavior of both NAPL and soil phases in the process of the risk assessment of the sites contaminated with petroleum hydrocarbons.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.15 no.3
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• pp.199-206
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• 2017

A geological repository for the disposal of high-level radioactive waste is generally constructed in host rock at depths of 500~1,000 meters below the ground surface. A geological repository system consists of a disposal canister with packed spent fuel, buffer material, backfill material, and intact rock. The buffer is indispensable to assure the disposal safety of high-level radioactive waste, and it can restrain the release of radionuclides and protect the canister from the inflow of groundwater. Since high temperature in a disposal canister is released to the surrounding buffer material, the thermal properties of the buffer material are very important in determining the entire disposal safety. Even though there have been many studies on thermal conductivity, there have been only few studies that have investigates the specific heat capacity of the bentonite buffer. Therefore, this paper presents a specific heat capacity prediction model for compacted Gyeongju bentonite buffer material, which is a Ca-bentonite produced in Korea. Specific heat capacity of the compacted bentonite buffer was measured using a dual probe method according to various degrees of saturation and dry density. A regression model to predict the specific heat capacity of the compacted bentonite buffer was suggested and fitted using 33 sets of data obtained by the dual probe method.

• Geomechanics and Engineering
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• v.22 no.4
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• pp.291-303
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• 2020

The deformation of the rock surrounding a tunnel manifests due to the stress redistribution within the surrounding rock. By observing the deformation of the surrounding rock, we can not only determine the stability of the surrounding rock and supporting structure but also predict the future state of the surrounding rock. In this paper, we used grey system theory to analyse the factors that affect the deformation of the rock surrounding a tunnel. The results show that the 5 main influencing factors are longitudinal wave velocity, tunnel burial depth, groundwater development, surrounding rock support type and construction management level. Furthermore, we used seismic prospecting data, preliminary survey data and excavated section monitoring data to establish a neural network learning model to predict the total amount of deformation of the surrounding rock during tunnel collapse. Subsequently, the probability of a change in deformation in each predicted section was obtained by using a Bayesian method for detecting change points. Finally, through an analysis of the distribution of the change probability and a comparison with the actual situation, we deduced the survey mark at which collapse would most likely occur. Surface collapse suddenly occurred when the tunnel was excavated to this predicted distance. This work further proved that the Bayesian method can accurately detect change points for risk evaluation, enhancing the accuracy of tunnel collapse forecasting. This research provides a reference and a guide for future research on the probability analysis of tunnel collapse.

• The Journal of Engineering Geology
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• v.18 no.2
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• pp.207-214
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• 2008

We conducted uniaxial consolidation tests in mudstone samples with different clay content, in order to investigate time-dependent deformation and its characteristics. A significant amount of time-dependent strain was observed at a constant stress level immediately after a jump of stress was applied. For a given mudstone, the amount of time-dependent deformation was nearly proportional to the increment of stress, suggesting a linear viscous rheology. The amount of time-dependent strain increases with clay content, implying that clay plays an important role in creep of the unconsolidated mudstone. A power-law model was suitably applied to our results, suggesting that a short-term prediction of time-dependent deformation of the mudstone is tentatively feasible.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.520-520
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• 2023

제주도 동부 중산간 지역은 화산암으로 구성된 지하지질로 인해 지하수위의 변동폭이 크고 변동양상이 복잡하여 인공신경망(Artificial Neural Network, ANN) 모델 등을 활용한 지하수위의 예측이 어렵다. ANN에 적용되는 활성화함수에 따라 지하수의 예측성능은 달라질 수 있으므로 활성화함수의 비교분석 후 적절한 활성화함수의 사용이 반드시 필요하다. 본 연구에서는 5개 활성화함수(sigmoid, hyperbolic tangent(tanh), Rectified Linear Unit(ReLU), Leaky Rectified Linear Unit(Leaky ReLU), Exponential Linear Unit(ELU))를 제주도 동부 중산간지역에 위치한 2개 지하수 관정에 대해 비교분석하여 최적 활성화함수 도출을 목표로 한다. 또한 최적 활성화함수를 활용한 ANN의 적용성을 평가하기 위해 최근 널리 사용되고 있는 순환신경망 모델인 Long Short-Term Memory(LSTM) 모델과 비교분석 하였다. 그 결과, 2개 관정 중 지하수위 변동폭이 상대적으로 큰 관정은 ELU 함수, 상대적으로 작은 관정은 Leaky ReLU 함수가 지하수위 예측에 적절하였다. 예측성능이 가장 낮은 활성화함수는 sigmoid 함수로 나타나 첨두 및 최저 지하수위 예측 시 사용을 지양해야 할 것으로 판단된다. 도출된 최적 활성화함수를 사용한 ANN-ELU 모델 및 ANN-Leaky ReLU 모델을 LSTM 모델과 비교분석한 결과 대등한 지하수위 예측성능을 나타내었다. 이것은 feed-forward 방식인 ANN 모델을 사용하더라도 적절한 활성화함수를 사용하면 최신 순환신경망과 대등한 결과를 도출하여 활용 가능성이 충분히 있다는 것을 의미한다. 마지막으로 LSTM 모델은 가장 적절한 예측성능을 나타내어 다양한 인공지능 모델의 예측성능 비교를 위한 기준이 되는 참고모델로 활용 가능하다. 본 연구에서 제시한 방법은 지하수위 예측과 더불어 하천수위 예측 등 다양한 시계열예측 및 분석연구에 유용하게 사용될 수 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.449-449
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• 2022

제주도는 용수의 대부분을 지하수에 의존하므로 지하수위의 예측 및 관리는 매우 중요한 사항이다. 제주도의 지층은 화산활동에 의한 현무암이 겹겹이 쌓여있는 형태를 나타내며 육지의 지층구조와 매우 다른 복잡한 형태를 나타낸다. 이에 따라 제주도 지하수위의 예측은 매우 난해하며, 최근에는 딥러닝 인공지능 모델을 활용하여 지하수위를 예측하는 연구사례가 증가하고 있다. 기존의 연구들은 인공지능 모델들이 지하수위를 적절히 예측한다고 보고하고 있으나 예측의 적절성에 대한 판단기준을 제시하지 못하였으므로 이에 대한 명확한 제시가 필요하다. 본 연구의 목표는 인공지능을 활용한 지하수위 예측오차가 허용 가능한지 판단할 수 있는 기준을 제시함에 있다. 이를 위해 전 세계의 과거 20년 동안 관련 연구결과들을 수집 및 분석하였으며, 분석 결과 인공지능 모델의 지하수위 예측오차는 지하수위 변동성이 큰 지역일수록 증가하는 것을 확인하였다. 이것은 지하수위의 변동형태가 크고 복잡할수록 인공지능 모델의 지하수위 예측성능은 낮아진다는 것을 의미한다. 이 관계를 명확하게 나타내기 위해 지하수위 최대변동폭과 평균제곱근오차 및 최대오차와의 관계를 선형회귀식으로 도출하여 허용가능한 예측오차 기준을 제시하였다. 그리고 기존 연구들에서 제시한 Nash-Sutcliffe 효율성지수와 결정계수를 분석하여 선형회귀식에 의한 기준을 보완할 수 있는 추가적인 기준을 제시하였다. 본 연구에서 제시한 인공지능 모델에 의한 지하수위 예측결과의 적절성 판단기준은 향후 지속적으로 증가하는 인공지능 예측연구에 유용하게 사용될 수 있다.

• Journal of the Korean earth science society
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• v.41 no.1
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• pp.31-39
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• 2020

Cheonji, the largest caldera lake in Asia, is located at the summit of Baekdu Mountain. Cheonji is covered with snow and ice for about six months of the year due to its high altitude and its surrounding environment. Since most of the sources of water are from groundwater, the water temperature is closely related to the volcanic activity. However, in the 2000s, many volcanic activities have been monitored on the mountain. In this study, we analyzed the dimension of ice produced during winter in Baekdu Mountain using Sentinel-1 satellite image data provided by the European Space Agency (ESA). In order to calculate the dimension of ice from the backscatter image of the Sentinel-1 satellite, 20 Gray-Level Co-occurrence Matrix (GLCM) layers were generated from two polarization images using texture analysis. The method used in calculating the area was utilized with the Support Vector Machine (SVM) algorithm to classify the GLCM layer which is to calculate the dimension of ice in the image. Also, the calculated area was correlated with temperature data obtained from Samjiyeon weather station. This study could be used as a basis for suggesting an alternative to the new method of calculating the area of ice before using a long-term time series analysis on a full scale.