• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.869-887
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• 2008

Coffer dam for tunnel type spillway in inflow section of Dae-am dam was originally planned as 2 lines sheet piles with Water Zet method. But, the result of pilot test was caused of some problems that vibration during installation of pile could pollute water and water leakage could the lower part. So, sheet piles was not satisfactory for faculty of coffer dam. Structural instability of sheet pile system need to reinforcement. Characteristic of Dae-am dam was small reservoir capacity but wide drainage area, of which it was judgment that security of leakage and stability was difficult during excavation of inlet part. So, we consider that water curtain method utilized with in site pouring concrete pile method was designed at weir part of spillway. We were known about basement rock that geological boring was carried out in weir part. After taking a deep consideration, PRD method was accepted as a new method. Concrete pile by PRD was installed to below country rock. CJM method was carried out with PRD. After making concrete wall using Top-down method, earth anchors were installed for supporting it. According to the result of numerical analysis, as water level rises, wall is stable.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.118-118
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• 2021

최근 지구 온난화 현상으로 인한 기후변화로 이상기후 현상이 발생하고 있으며 이로 인해 장기적으로 폭염의 빈도 및 강도 상승에 따른 가뭄 피해 우려가 증가하고 있다. 농업 가뭄은 강수량 부족, 토양 수분 부족, 저수량 부족 등 농업분야에 영향을 주는 인자들과 관련되어 있어 농작물 생육 및 수확량 감소를 야기한다. 우리나라는 논농사가 주를 이루고 있어 국내 농업 가뭄은 주수원공인 농업용 저수지의 가용저수용량으로 판단 가능하다. 따라서 안정적인 농업용수 공급을 위해 수리시설물의 모니터링, 공급량 등의 분석이 이루어져야 하며, 농업 가뭄에 대비하기 위해 농업용 저수지의 가용저수용량 파악이 필요하다. 수자원 분야에서 지점자료의 시·공간적 한계점을 보완하기 위해 인공위성 자료를 활용한 연구가 활발히 이루어지고 있으며, 본 연구에서는 위성영상 자료 및 딥러닝 기반 알고리즘을 적용하여 농업용 저수지 수표면 탐지 및 시계열 분석을 목적으로 한다. 위성영상 자료는 5일 주기 및 10 m 공간해상도를 가진 Sentinel-2 위성영상 자료를 활용하고자 하였으며, 딥러닝에 적용하기 위하여 100장 이상의 영상 이미지를 구축하였다. 딥러닝 기반 알고리즘으로는 Convolutional Neural Network (CNN)을 활용하였으며, CNN은 주로 이미지 분류나 객체 검출 문제를 해결하기 위해 제안된 모델로 최근 픽셀 단위로 분류가 가능한 알고리즘이 개발되어 높은 정확도의 수표면 탐지가 가능할 것으로 판단된다. 따라서 본 연구에서는 CNN 기반 수표면 탐지 알고리즘을 개발하여 Sentinel-2 영상 기준 경기도 안성시를 대상으로 소규모 농업용 저수지의 수표면적에 대한 시계열 데이터를 분석하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.297-299
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• 2021

Predicting future productivity of tight oil is an important task for analyzing residual oil recovery and reservoir behavior. In general, productivity prediction is made using the decline curve analysis(DCA). In this study, we intend to propose an effective model for predicting future production using deep learning-based recurrent neural networks(RNN), LSTM, and GRU algorithms. As input variables, the main parameters are oil, gas, water, which are calculated during the production of tight oil, and the type curve calculated through various cluster analyzes. the output variable is the monthly oil production. Existing empirical models, the DCA and RNN models, were compared, and an optimal model was derived through hyperparameter tuning to improve the predictive performance of the model.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.483-483
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• 2022

댐과 저수지의 운영 최적화를 위한 수문학적 예보는 현재 수동적인 댐 운영이 주를 이루면서 활용도가 높지 않다. 불확실한 기후변화나 기후재난 상황에서 우리 사회에 악영향을 최소화하기 위해 선제적으로 대응/대비할 수 있는 댐 운영 방안이 불가피하다. 강우량 예측 기술은 기후변화로 인해 제한적인 상황이다. 실례로, 2020년 8월에 섬진강의 댐이 극심한 집중 강우로 인해 무너지는 사태가 발생하였고 이로 인해 지역사회에 막대한 경제적 피해가 발생하였다. 선제적 댐 방류량 운영 기술은 또한 환경적인 변화로 인한 영향을 완화하기 위해 필요한 것이다. 제한적인 기상 예보 기술을 극복하고자 심화학습이나 강화학습 같은 인공지능 모델들의 활용성에 대한 연구가 시도되고 있다. 따라서 본 연구는 섬진강 댐의 시간당 수문 데이터를 이용하여 댐 운영을 위한 심화학습 모델을 개발하고 그 활용도를 평가하였다. 댐 운영을 위한 심화학습 모델로서 시계열 데이터 예측에 적합한 Long Sort Term Memory(LSTM)과 Gated Recurrent Unit(GRU) 알고리즘을 구축하고 댐 수위를 예측하였다. 분석 자료는 WAMIS에서 제공하는 2000년부터 2021년까지의 시간당 데이터를 사용하였다. 입력 데이터로서 시간당 유입량, 강우량과 방류량을, 출력 데이터로서 시간당 수위 자료를 각각 사용하였으며. 결정계수(R2 Score)를 통해 모델의 예측 성능을 평가하였다. 댐 수위 예측값 개선을 위해 하이퍼파라미터의 '최적값'이 존재하는 범위를 줄여나가는 하이퍼파라미터 최적화를 두 가지 방법으로 진행하였다. 첫 번째 방법은 수동적 탐색(Manual Search) 방법으로 Sequence Length를 24, 48, 72시간, Hidden Layer를 1, 3, 5개로 설정하여 하이퍼파라미터의 조합에 따른 LSTM와 GRU의 민감도를 평가하였다. 두 번째 방법은 Grid Search로 최적의 하이퍼파라미터를 찾았다. 이 두가지 방법에서는 같은 하이퍼파라미터 안에서 GRU가 LSTM에 비해 더 높은 예측 정확도를 보였고 Sequence Length가 높을수록 정확도가 높아지는 경향을 보였다. Manual Search 방법의 경우 R2가 최대 0.72의 정확도를 보였고 Grid Search 방법의 경우 R2가 0.79의 정확도를 보였다. 본 연구 결과는 가뭄과 홍수와 같은 물 재해에 사전 대응하고 기후변화에 적응할 수 있는 댐 운영 개선에 도움을 줄 수 있을 것으로 판단된다.

• Geophysics and Geophysical Exploration
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• v.7 no.3
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• pp.164-173
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• 2004

To delineate geothermal water movement at the Pohang geothermal development site, Self-Potential (SP) survey and monitoring were carried out during pumping tests. Before drilling, background SP data have been gathered to figure out overall potential distribution of the site. The pumping test was performed in two separate periods: 24 hours in December 2003 and 72 hours in March 2004. SP monitoring started several days before the pumping tests with a 128-channel automatic recording system. The background SP survey showed a clear positive anomaly at the northern part of the boreholes, which may be interpreted as an up-flow Bone of the deep geothermal water due to electrokinetic potential generated by hydrothermal circulation. The first and second SP monitoring during the pumping tests performed to figure out the fluid flow in the geothermal reservoir but it was not easy to see clear variations of SP due to pumping and pumping stop. Since the area is covered by some 360 m-thick tertiary sediments with very low electrical resistivity (less than 10 ohm-m), the electrokinetic potential due to deep groundwater flow resulted in being seriously attenuated on the surface. However, when we compared the variation of SP with that of groundwater level and temperature of pumping water, we could identify some areas responsible to the pumping. Dominant SP changes are observed in the south-west part of the boreholes during both the preliminary and long-term pumping periods, where 3-D magnetotelluric survey showed low-resistivity anomaly at the depth of $600m\~1,000m$. Overall analysis suggests that there exist hydraulic connection through the southwestern part to the pumping well.

• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1181-1191
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• 2013

This study have implemented finding the optimal water temperature parameter set for Hapcheon dam reservoir using CE-QUAL-W2 model. In particular the sensitivity analysis was carried out for four water temperature parameters of wind sheltering coefficient (WSC), radiation heat coefficient (BETA), light extinction coefficient (EXH2O), heat exchange coefficient at the channel bed (CBHE). Firstly, WSC, BETA, EXH2O shows relatively high sensitivity in common during April to September, and CBHE does during August to November. Secondly, as a result of identifying depth range of parameter influence, BETA and EXH2O show 0~9 m and 8~14 m which is thermocline layer close to water surface, CBHE is deep layer 12 m away from bottom. Finally, applying annual or monthly optimal parameter sets indicates that the bias between two sets does not show much differences for WSC and CBHE parameters, but BETA and EXH2O parameters show $0.20^{\circ}C$ and $0.51^{\circ}C$ of monthly average biases for two parameter sets. In particular the bias reveals to be $0.4^{\circ}C$ and $1.09^{\circ}C$ during May and August that confirms the necessity of use of monthly parameters during that season. It is claimed that the current operational custom use of annual parameters in calibration of reservoir water quality model requires the improvement of using monthly parameters.

• Korean Journal of Ecology and Environment
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• v.55 no.4
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• pp.274-284
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• 2022

Through sample-size-based rarefaction analyses, we tried to suggest the appropriate degree of sample concentration and sub-sample extraction, as a way to estimate more accurate zooplankton species diversity when assessing biodiversity. When we collected zooplankton from three reservoirs with different environmental characteristics, the estimated species richness (S) and Shannon's H' values showed different changing patterns according to the amount of sub-sample extracted from the whole sample by reservoir. However, consequently, their zooplankton diversity indices were estimated the highest values when analyzed by extracting the largest amount of sub-sample. As a result of rarefaction analysis about sample coverage, in the case of deep eutrophic reservoir (Juam) with high zooplankton species and individual numbers, it was analyzed that 99.8% of the whole samples were represented by only 1 mL of sub-sample based on 100 mL of concentrated samples. On the other hand, in Soyang reservoir, which showed very small species and individual numbers, a relatively low representation at 97% when 10 mL of sub-sample was extracted from the same amount of concentrated sample. As such, the representation of sub-sample for the whole zooplankton sample varies depending on the individual density in the sample collected from the field. If the degree of concentration of samples and the amount of sub-sample extraction are adjusted according to the collected individual density, it is believed that errors that occur when comparing the number of species and diversity indices among different water bodies can be minimized.

• The Journal of Engineering Geology
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• v.34 no.2
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• pp.229-248
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• 2024

This investigated the hydrogeochemical and isotopic characteristics of geothermal waters, groundwaters, and surface waters in Dongrae-gu, Busan, South Korea, in order to determine the origins of the salinity components in the geothermal waters, and their formation mechanisms and heat sources The geothermal waters are Na-Cl-type, distinct from surrounding groundwaters (Na-HCO₃^- and, Ca-HCO₃^- (SO₄, Cl)-type) and surface waters (Ca-HCO₃(SO₄, Cl)-type). This indicates the geothermal waters formed at depth as compared with the groundwaters. δ¹⁸O and δD values of the geothermal waters are relatively depleted as compared with the groundwaters, due to altitude effects and deep circulation of the geothermal waters. Helium and neon isotope ratios (³ He/⁴He and, ⁴He/²⁰Ne) of the geothermal waters plot on a single mixing line between mantle (³He = 3.76~4.01%) and crust (⁴He = 95.99~96.24 %), indirectly suggesting that the heat source is due to the decay of radioactive elements in rocks. The geothermal reservoir temperatures were calculated using the silica-enthalpy and Giggenbach models, yielding values of 82~130℃, and the depth of the geothermal reservoir is estimated to be 1.7~2.9 km below the surface. The correlation between Cl/Na and Cl/HCO₃ for the Dongrae geothermal waters requires the input of salty water. The supply of saline composition is interpreted due to the dissolution of residual paleo-seawater.

• Economic and Environmental Geology
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• v.48 no.4
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• pp.301-312
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• 2015

A commercial gas field was found in the southwestern continental shelf of the Ulleung Basin, East Sea in the late 1990s. To develop additional gas field, an exploration well was drilled through the coarse infill of submarine canyon near the gas field, but it was uneconomic to develop hydrocarbons. Using newly acquired deep seismic reflection and previous well data, we have identified additional geological structure which has hydrocarbon potentials below submarine canyons in the southwestern margin of the basin. Based on the interpretation of the deep seismic reflection and well data, the sequences of the study area can be classified into the syn-rift megasequence(MS1), post-rift megasequence(MS2), syn-compressional megasequence(MS3), and post-compressional megasequence(MS4) in relation to the tectonic events. MS1, deposited simultaneously with the basin formation before the middle Miocene, is characterized by chaotic seismic facies with low- to moderate-amplitude and low frequency reflections. MS2 comprises laterally continuous, low- to moderate-amplitude reflections, showing progradational stacking patterns due to high rates of sediment supply during basin expansion in the middle Miocene. MS3 is mainly composed of continuous reflections with high amplitude and moderate- to high-frequency which are interpreted as coarse-grained sediments. The coarse-grained sediments of MS3 sequence is widely truncated by several submarine canyons which filled with fine-grained sediment of MS4 to form a stratigraphic trap of hydrocarbon. Therefore, the reservoir and seal of the hydrocarbon trap in the study area are coarse-grained sediment of MS3 and submarine canyon filled with fine-grained sediment of MS4, respectively. A flat-spot seismic anomaly, which may indicate the presence of hydrocarbon, is observed within the stratigraphic trap.