• Journal of Environmental Impact Assessment
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• v.20 no.2
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• pp.227-234
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• 2011

In the study, CE-QUAL-W2 was used and its examination and correction were conducted targeting 2001 and 2003 when the condition of rainfall was contradicted. Using the proved model in 2003, a scenario was implemented with management of locations for dewatering outlets and actual data for dam management in 1987 when inflow and outflow level were almost same. In case of the scenario which the location of dewatering outlets was 5m higher than usual location, exclusion efficiency for turbid water inflow at the beginning of precipitation was good. In case of the scenario which the location of dewatering outlets was 10m lower than usual location, exclusion efficiency for excluding turbid water remained in a reservoir after the end of precipitation. However, the scenario applying dam management data in 1987, exclusion efficiency was relatively low. In the scenario, power-generating water release spot at EL.57m for first four days after the beginning of precipitation, EL.52m for 5th to 8th and EL.42m from 9th days. An analysis of the scenario reveals that both excessive days exceeded 30 NTU and average turbidity levels were decreased comparing before and after the alteration on outlets. The average turbidity levels were decreased by minimum of 55% to maximum of 70% and 30NTU exceeding days were decreased by 45 days at maximum. Also, since it could exclude most of turbid water in a reservoir before the destatifcation, the risk for turbid water evenly distributed in a reservoir along with turn-over could be decreased as well.

• Journal of Ocean Engineering and Technology
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• v.27 no.2
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• pp.107-113
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• 2013

Most natural river reach are approximately balanced with respect to sediment inflow and outflow. Dam construction dramatically alters this balance, creating an impounded river reach characterized by extremely low flow velocities and efficient sediment trapping. The impounded reach will accumulate sediment and lose storage capacity until a balance is again achieved, which would normally occur after the impoundment has become "filled up" with sediment and can no longer provide water storage and other benefits. This paper aims to investigate the sediment removal process in dam reservoir using simultaneously pressure flushing operation and vibrator machine. The main objective of this study is to identify the effect of vibrator in flushing cone dimensions. To achieve the objectives of present study, laboratory test have conducted under different hydraulic conditions such as two bottom outlets with diameter equal to 2" and 3", five discharges 0.23, 0.53, 1.21, 1.53 and 2.1 lit/s and only one water depth above the center of bottom outlets. Using the vibrator machine mounted into the reservoir and close to the bottom outlet, different frequency e.g. 20, 35 and 50 HZ, have been introduced to the deposited sediment at the vicinity of outlet. The results indicate that the volume and width of flushing cone are strongly affected by frequency of vibrations. The results indicate that the volume and width of flushing cone are strongly affected by frequency of vibrations.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.515-526
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• 2021

The frequency of typhoons and torrential rainfalls has increased due to climate change, and the concurrent risk of breakage of dams and reservoirs has increased due to structural aging. To cope with the risk of dam breakage, a more accurate emergency action plan (EAP) must be established, and more advanced technology must be developed for the prediction of flooding. Hence, the present study proposes a method for establishing a more effective EAP by performing flood and inundation analyses using one- and two-dimensional models. The probable maximum flood (PMF) under the condition of probable maximum precipitation (PMP) was calculated for the target area, namely the Gyeong-cheon reservoir watershed. The breakage scenario of the Gyeong-cheon reservoir was then built up, and breakage simulations were conducted using the dam-break flood forecasting (DAMBRK) model. The results of the outflow analysis at the main locations were used as the basis for the one-dimensional (1D) and two-dimensional (2D) flood inundation analyses using the watershed modeling system (WMS) and the FLUvial Modeling ENgine (FLUMEN), respectively. The maximum inundation area between the Daehari-cheon confluence and the Naeseong-cheon location was compared for each model. The 1D flood inundation analysis gave an area of 21.3 km², and the 2D flood inundation analysis gave an area of 21.9 km². Although these results indicate an insignificant difference of 0.6 km² in the inundation area between the two models, it should be noted that one of the main locations (namely, the Yonggung-myeon Administrative and Welfare Center) was not inundated in the 1D (WMS) model but inundated in the 2D (FLUMEN) model.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.478-481
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• 2005

본 연구에서는 댐 건설로 인한 수자원환경이 변화된 낙동강 유역에 대해 국내외 다양한 연구 성과를 검토하여 CE-QUAL-RIV1모형을 이용한 비정상상태의 수질예측을 위한 최적시스템을 구성하였다. 수질매개변수에 대한 민감도 분석은 절대량의 변화를 도시하는 방법을 이용하였으며 구축된 수리 및 수질예측 통합 모형을 2001-2002년에 걸쳐 낙동강 유역의 실측자료를 이용하여 검증 및 보정을 실시하였다. 낙동강 유역의 주요 지점의 실측치 및 하류부 취수장에서의 일별 실측치와의 비교검토를 통해, 본 연구 모형의 적용성을 입증하였고, 댐 방류영향 및 지류의 무처리하수 유입시의 각 댐의 방류영향을 검토하였다. 본 연구를 통한 댐방류영향을 포함한 하천수질예측 모형체제 구축은 하천 수질 및 생태계의 수학적 표현을 통해 장래의 수질을 예측하고, 예측된 결과에 따라 합리적인 수질관리대책을 수립하는데 크게 기여할 수 있을 것이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1580-1584
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• 2008

친수환경으로서의 수변공간의 활용은 위락공간 및 자연환경으로서 도시민의 삶의 질 향상에 매우 큰 의미를 지닌다. 서울시민의 대표적인 친수환경 공간인 한강시민공원은 조성 이후 이용자에게 위락 및 자연공간으로서 그 역할을 다하여 왔으나 최근에 급증하고 있는 이상기상현상과 국지적 집중호우의 증가에 따라 도시지역 및 상류지역의 홍수 발생시에는 한강시민공원의 폐쇄와 함께 이용자의 접근을 사전통제하거나 신속하게 대피시켜 안전을 도모하여야 한다. 따라서 본 연구에서는 한강시민공원이 침수되는 상황을 모의분석하기 위해 필요한 각종 기본 자료와 매개변수에 대한 고찰을 실시하였고, 팔당댐 방류량에 따른 1차원 및 2차원 수치모형을 통한 한강시민공원의 홍수위 영향을 분석하였다. 본 연구에서 분석한 결과는 홍수 발생시 한강시민공원의 합리적인 이용 및 관리와 이용자의 안전 및 비상대처계획 등의 수립에 있어서 중요한 자료로 활용될 수 있을 것이다.

• Journal of Korean Society of Water and Wastewater
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• v.24 no.5
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• pp.549-560
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• 2010

Recently, heavy rainfalls due to the climate change in Korea have caused inundation problems in urban sewer networks. In july 2006, a flooding accident at Misari motorboat racing stadium near the Han river occurred due to the effect of record-breaking outflow discharge from Paldang-dam. The purpose of this study was to simulate and analyze the flooding accident at Misari stadium by MOUSE model. The results of simulation analysis indicated that the total flood volume was $1,313,450m^3$. The effect of back water was 85.9% of the total volume which was caused by the manhole accident, and the effect of accumulated runoff was 14.1% of total volume which was caused by non-return valve shutdown. The simulation results of this MOUSE modeling that was linked to the boundary condition of the dynamic flows in the river by DWOPER model showed the potential of successful inundation analysis for sewer networks.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.976-976
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• 2012

The applicability of a distributed rainfall-runoff model for large river basin flood forecasts is analyzed by applying the model to the Nakdong River basin. The spatially explicit hydrologic model was constructed and calibrated by the several storm events. The assimilation of the large scale Nakdong River basin were conducted by calibrating the sub-basin channel outflow, dam discharge in the basin rainfall-runoff model. The applicability of automatic and semi-automatic calibration methods was analyzed for real time calibrations. Further an ensemble distributed rainfall runoff model has been developed to measure the runoff hydrograph generated for any temporally-spatially varied rainfall events, also the runoff of basin can be forecast at any location as well. The results of distributed rainfall-runoff model are very useful for flood managements on the large scale basins. That offer facile, realistic management method for the avoiding the potential flooding impacts and provide a reference for the construct and developing of flood control facilities.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.159-159
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• 2019

In this study, the simulation of sediment deposition at Sangju weir reservoir, South Korea, was carried out using artificial neural networks. The ANNs have typically been used in water resources engineering problems for their robustness and high degree of accuracy. Three basic variables namely turbid water inflow, outflow, and water stage have been used as input variables. It was found that ANNs were able to establish valid relationship between input variables and target variable of sedimentation. The R value was 0.9806, 0.9091, and 0.8758 for training, validation, and testing phase respectively. Comparative analysis was also performed to find optimum structure of ANN for sediment deposition prediction. 3-14-1 network architecture using BR algorithm outperformed all other combinations. It was concluded that ANN possess mapping capabilities for complex, non-linear phenomenon of reservoir sedimentation.

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.755-765
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• 2013

This study developed a real-time dam's hydrologic variables prediction model (DHVPM) and evaluated its performance for simulating historical dam inflow and outflow in the Chungju dam basin. The DHVPM consists of the Sejong University River Forecast (SURF) model for hydrologic modeling and an autoreservoir operation method (Auto ROM) for dam operation. SURF model is continuous rainfall-runoff model with data assimilation using an ensemble Kalman filter technique. The four extreme events including the maximum inflow of each year for 2006~2009 were selected to examine the performance of DHVPM. The statistical criteria, the relative error in peak flow, root mean square error, and model efficiency, demonstrated that DHVPM with data assimilation can simulate more close to observed inflow than those with no data assimilation at both 1-hour lead time, except the relative error in peak flow in 2007. Especially, DHVPM with data assimilation until 10-hour lead time reduced the biases of inflow forecast attributed to observed precipitation error. In conclusion, DHVPM with data assimilation can be useful to improve the accuracy of inflow forecast in the basin where real-time observed inflow are available.