• Water Engineering Research
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• v.3 no.2
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• pp.143-153
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• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.3
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• pp.357-365
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• 2004

Effect of artificial circulation on amelioration of water quality in Yeoncho Lake was analyzed using summer data between 1991-2002. Two sites, intake tower area where aeration systems are concentrated in and near the inlet of reservoir were selected for comparison in this study. Summer averages between may and september Showed that aeration system might be beneficial in the improvement of water quality of BOD5, COD, and TN while Chl-a concentration and transparency did show opposite pattems. Wilcoxon's singed rank test for matched pair indicated slight increase of BOD5 and COD concentrations in the vicinity of intake tower while other variables did not show any significant differences from data of inlet of reservoir. During the study, it was found that the following subjects need to be investigated for more detailed analysis. 1) Dynamic pollutant loading from outside and inside the lake, 2) Biological, Chemical and Physical lake data when aeration systems are in and not in operation and 3) Radius of influence of aeration system.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.167-167
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• 2020

Annual sediment deposition in reservoirs behind weirs poses flood risk, while its accurate prediction remains a challenge. Sediment management by hydraulic flushing is an effective method to maintain reservoir storage. In this study, an integrated approach to predict sediment inflow and sediment flushing simulation in reservoirs is presented. The annual sediment inflow prediction was carried out with Artificial Neural Networks (ANN) modelling. RESCON model was applied for quantification of sediment flushing feasibility criteria. The integrated approach was applied on Sangju Weir and also on estuary of Nakdong River (NREB). The mean annual sediment inflow predicted at Sangju Weir and NREB was 400,000 ㎥ and 170,000 ㎥, respectively. The sediment characteristics gathered were used to setup RESCON model and sediment balance ratio (SBR) and long term capacity ratio (LTCR) were used as flushing efficiency indicators. For Sangju Weir, the flushing discharge, Q_f = 140 ㎥/s with a drawdown of 5 m, and flushing duration, T_f = 10 days was necessary for efficient flushing. At NREB site, the parameters for efficient flushing were Q_f = 80 ㎥/s, T_f = 5 days, N = 1, El_f = 2.24 m. The hydraulic flushing was concluded feasible for sediment management at both Sangju Weir and NREB.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.11a
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• pp.33-45
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• 2006

The interest of sediment has been increased daily because most of domestic dam reservoir's operation time have been extended and wide basin area is the main characteristics for artificial reservoir which the speed of water flow in artificial reservoir is slower than that of natural reservoir. Therefore a lot of sediment has been significantly accumulated. In this study, the accurate topographic data were obtained using echo-sounding system. GPS survey, low-frequency sub-bottom profiler, and high-frequency echo-sounding system were used to compute the exact amount of sediment. Based on the results, DEM(Digital Elevation Model) and DSM(Digital Surface Model) were generated. The GIS system for the management of sediment was created based on topographic data on the riverbed and this system can be efficiently used for the management of sediment which caused the problems of reservoir capacity and water quality.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.1-12
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• 2022

Estuary reservoirs were artificial reservoir with seawalls built at the exit points of rivers. Although many water resources can be saved, it is difficult to manage due to the large influx of pollutants. To manage this, it is necessary to analyze watersheds and reservoirs through accurate modeling. Therefore, in this study, we linked the Hydrological Simulation Program-FORTRAN (HSPF), Environmental Fluid Dynamics Code (EFDC), and Water quality Analysis Simulation Program (WASP) models to simulate the hydrology and water quality of the watershed and the water level and quality of estuary lakes. As a result of applying the linked model in stream, R² 0.7 or more was satisfied for the watershed runoff except for one point. In addition, the water quality satisfies all within 15% of PBIAS. In reservoir, R² 0.72 was satisfied for water level and the water quality was within 15% of T-N and T-P. Through the modeling system, We applied upstream pollutant management scenarios to analyze changes in water quality in estuary reservoirs. Three pollution source management were applied as scenarios, the improvement of effluent water quality from the sewage treatment plant and the livestock waste treatment plant was effective in improving the quality of the reservoir water, while the artificial wetland had little effect. Water quality improvement was confirmed as a measure against upstream pollutants, but it was insufficient to achieve agricultural water quality, so additional reservoir management is required.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.4
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• pp.445-452
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• 2004

Intermittent deep draw artificial circulation system is one of the most widely used destratification systems to control algal bloom in reservoirs in Korea. However, there have been neither theoretical background of design criteria nor operation guide line for efficient application of the system available for such systems. A design method was developed to calculate required compressor capacity and number of circulation units considering physical interactions between stratified water layers and plumes induced by the intermittent deep draw artificial circulation system. The program was tested with data observed in Yeoncho Lake. The results indicated that the developed method can applied in the fields successfully. Further validation processes would improve design and operation methods.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.4 no.2
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• pp.84-91
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• 2001

This study was carried out to develop the technology of artificial floating island for the wild-life habitat at the reservoir of Korea University farm near Seoul. After the execution of an artificial floating island with 6 cells(each $3{\times}3m$), each cell was planted with 5 different species and one mixed of them, to the reservoir in 1999 through 2000. The monitored results were as follows; 1. Typha orientalis, Zizania latifolia and Oenanthe japonica were died back, but Phragmites communis, Phragmites japonica and Juncus effusus var. decipiens were well growing. 2. The limits of sinking water depth of the planting foundation were different with the plant species, that is, 40cm to the Juncus effusus var. dicipiens and 50cm to Phragmites communis. Accordingly the water depth should be kept differently with each species. 3. 33 species of fauna were monitored in the first year(1999) and 43 species in the second (2000) increasingly. 4. For the more wild-lives inducing to the artificial floating island, establishing the eco-corridor from the surrounding environment was needed.

• Journal of Soil and Groundwater Environment
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• v.16 no.6
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• pp.34-45
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• 2011

For the Hancheon drainage area in Jeju island, a groundwater flow model using Visual MODFLOW was developed to simulate artificial recharge through injection wells installed in the Hancheon reservoir. The model was used to analyze changes of the groundwater level and the water budget due to the artificial recharge. The model assumed that $2{\times}10^6m^3$ of storm water would recharge annually through the injection wells during the rainy season. The transient simulation results showed that the water level rose by 39.6 m at the nearest monitoring well and by 0.26 m at the well located 7 km downstream from the injection wells demonstrating a large extent of the affected area by the artificial recharge. It also shown that, at the time when the recharge ended in the 5th year, the water level increased by 81 m at the artificial reservoir and the radius of influence was about 2.1 km downstream toward the coast. The residence time of recharged groundwater was estimated to be no less than 5 years. The model also illustrated that 15 years of artificial recharge could increase the average linear velocity of groundwater up to 1540 m/yr, which showed 100 m/yr higher than before. Increase of groundwater storage due to artificial recharge was calculated to be $2.4{\times}10^6$ and $4.3{\times}10^6m^3$ at the end of the 5th and 10th years of artificial recharge, respectively. The rate of storage increase was gradually diminished afterwards, and storage increase of $5.0{\times}10^6m^3$ was retained after 15 years of artificial recharge. Conclusively, the artificial recharge system could augment $5.0{\times}10^6m^3$ of additional groundwater resources in the Hancheon area.

• 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.