• Journal of Korean Society on Water Environment
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• v.26 no.2
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• pp.231-242
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• 2010

The objective of study was to determine an optimal location of a float-type curtain weir in Daecheong Reservoir and to assess its effectiveness for the control of algal blooms in the reservoir. CE-QUAL-W2, a laterally averaged two-dimensional hydrodynamic and eutrophication model, was modified to accommodate vertical displacement of the weir according to water surface fluctuation and applied to simulate the reservoir hydrodynamics and water quality changes for the reservoir. The model calibrated in a previous study was updated and validated for different hydrological conditions representing drought year (2008) and normal year (2006) for the study, and adequately simulated the temporal and spatial variations of water temperature, nutrients and algal (Chl-a) concentrations. The effectiveness of curtain weir on the control of algal bloom was evaluated by applying the validated model to 2001 and 2006 assuming 9 scenarios for different installation locations. The reduction rates of algal concentration were placed in the range of 11.2~40.3% and 20.3~56.7% for 2001 and 2006, respectively. Although, the performance of curtain weir was slightly varied for different locations and different hydrological years, overall, the performance was improved as the weir was installed further downstream.

• Journal of Korea Water Resources Association
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• v.30 no.3
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• pp.269-278
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• 1997

Operating rules, the basic principle of reservoir operation, are mostly developed from maximum or minimum, mean inflow series so that those rules cannot be used in practical operating situations to estimate the expected benefits or provide the operating policies for uncertainty conditions. Many operating rules based on the deterministic method that considers all operation variables including inflows as known variables can not reflect to uncertainties of inflow variations. Explicit operating rules can be developed for improving the weakness. In this method, stochastic trend of inflow series, one of the reservoir operation variables, can be directly method, the stochastic technique was applied to develop reservoir operating rule. In this study, stochastic dynamic programming using the concepts was applied to develop optimal operating rule for the Chungju reservoir system. The developed operating rules are regarded as a practical usage because the operating policy is following up the basic concept of Lag-1 Markov except for flood season. This method can provide reservoir operating rule using the previous stage's inflow and the current stage's beginning storage when the current stage's inflow cannot be predicted properly.

• Environmental Engineering Research
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• v.21 no.2
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• pp.196-202
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• 2016

Membrane bioreactor (MBR) technology has previously been used by water industry to treat high salinity wastewater. In this study, an anoxic-oxic biofilm-membrane bioreactor (AOB-MBR) system has been developed to treat mustard tuber wastewater of 10% salinity (calculated as NaCl). To figure out the effects of operating conditions of the AOB-MBR on membrane fouling rate ($K_V$), response surface methodology was used to evaluate the interaction effect of the three key operational parameters, namely time interval for pump (t), aeration intensity ($U_{Gr}$) and transmembrane pressure (TMP). The optimal condition for lowest membrane fouling rate ($K_V$) was obtained: time interval was 4.0 min, aeration intensity was $14.6 m^3/(m^2{\cdot}h)$ and transmembrane pressure was 19.0 kPa. And under this condition, the treatment efficiency with different influent loads, i.e. 1.0, 1.9 and $3.3kgCODm^{-3}d^{-1}$ was researched. When the reactor influent load was less than $1.9kgCODm^{-3}d^{-1}$, the effluent could meet the third discharge standard of "Integrated Wastewater Discharge Standard". This study suggests that the model fitted by response surface methodology can predict accurately membrane fouling rate within the specified design space. And it is feasible to apply the AOB-MBR in the pickled mustard tuber factory, achieving satisfying effluent quality.

• IE interfaces
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• v.23 no.4
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• pp.311-318
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• 2010

In the daily multi-reservoir operating problem, monthly storage targets can be used as principal operational guidelines. In this study, we tested the use of a simple back-propagation Artificial Neural Network (ANN) model to derive monthly storage guideline for daily Coordinated Multi-reservoir Operating Model (CoMOM) of the Han-River basin. This approach is based on the belief that the optimum solution of the daily CoMOM has a good performance, and the ANN model trained with the results of daily CoMOM would produce effective monthly operating guidelines. The optimum results of daily CoMOM is used as the training set for the back-propagation ANN model, which is designed to derive monthly reservoir storage targets in the basin. For the input patterns of the ANN model, we adopted the ratios of initial storage of each dam to the storage of Paldang dam, ratios of monthly expected inflow of each dam to the total inflow of the whole basin, ratios of monthly demand at each dam to the total demand of the whole basin, ratio of total storage of the whole basin to the active storage of Paldang dam, and the ratio of total inflow of the whole basin to the active storage of the whole basin. And the output pattern of ANN model is the optimal final storages that are generated by the daily CoMOM. Then, we analyzed the performance of the ANN model by using a real-time simulation procedure for the multi-reservoir system of the Han-river basin, assuming that historical inflows from October 1st, 2004 to June 30th, 2007 (except July, August, September) were occurred. The simulation results showed that by utilizing the monthly storage target provided by the ANN model, we could reduce the spillages, increase hydropower generation, and secure more water at the end of the planning horizon compared to the historical records.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.1
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• pp.1-13
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• 2024

This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R² (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.61-70
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• 2018

This study examined how to determine the optimal sediment level in dam reservoir for efficient plan and operation of dam. Currently, Korea is applying a horizontally accumulated method for sediment level estimation for the safety design of dam and so the method estimated relatively higher level than others. However, the sediment level of dam reservoir should be accurately estimated because it is an important factor in assessing life cycle of a dam. The sediment level in dam reservoir can be determined by SED-2D model linked with RMA-2, horizontally accumulated method, area increment method, and empirical area reduction method. The estimated sediment level from each method was compared with the observed sediment level measured in 2007 in Imha dam reservoir, Korea and then the optimal method was determined. Also, the future sediment level was predicted by each method for the future trend analysis of sediment level. As the results, the most accurate sediment level was estimated by the empirical area reduction method and the future trend of sediment level variation followed the past trend. Therefore, we have found that the empirical area reduction method is a proper one for more accurate estimation of sediment level and it can be validated by the results from a numerical model of SED-2D linked with RMA-2 model.

• Spatial Information Research
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• v.12 no.1
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• pp.13-27
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• 2004

The current industrial development and the increase of population in Daecheong Reservoir basin have produced a rapid increase of wastewater discharge. This has resulted in problem of water quality control and management. Although many efforts have been carried out, reservoir water quality has not significantly improved. In this sense, the development of water quality management system is required to improve reservoir water quality. The goal of this study is to design a GIS-based water quality management system for the scientific water quality control and management in the Daecheong Reservoir. For general water quality analysis, WASP5 model was applied to the Daecheong Reservoir. A sensitivity analysis was made to determine significant parameters and an optimization was made to estimate optimal values. The calibration and verification were performed by using observed water quality data for Daecheong Reservoir. A water quality management system for Daecheong Reservoir was made by connecting the WASP5 model to ArcView. It allows a Windows-based Graphic User Interface(GUI) to implement all operation with regard to water quality analysis. The proposed water quality management system has capability for the on-line data process including water quality simulation, and has a post processor far the reasonable visualization for various output. The modeling system in this study will be an efficient NGIS(National Geographic Information System) far planning of reservoir water quality management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.6
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• pp.803-814
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• 2022

The Lao government is continuously developing hydro-power dams in addition to the existing eight power plants in the Nam Ngum River basin and is expanding the power capacity of the existing power plants to meet the expected increase in electricity demand. Accordingly, the Lao government has requested an update on the existing reservoir operating rule curve in order to run the power plants efficiently. To this end, this study reviewed the current independent operating system as well as the joint operating system in order to maximize the annual power generation produced by a power plant by using CSUDP, general-purpose dynamic programming (DP) software. The appropriate operating regulation curve forms (URC/LRC, MRC) were extracted from the DP results, and the annual power generations were simulated by inputting them as the basic operating data of the reservoir operation set of the HEC-ResSim program. By synthesizing the amount of the annual power generation simulated, the existing operation regulation curve, the operational performance, and the opinion of the field operator, the optimal reservoir operation regulation curves that maximize the annual power generation of the target power plant were developed. Results revealed that a system operating in conjunction with the reservoir produces about 2.5 % more power generation than an independent reservoir due to the synergistic effect of the connection.

• Proceedings of the KIEE Conference
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• 1986.07a
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• pp.274-277
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• 1986

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.218-219
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• 1990