• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.5
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• pp.94-102
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• 2000

Investigated was the effectiveness of a constructed wetland system on water quality in Hwa-Ong estruarin reservoir, located in Hwasung-Gun, Kyunggi-Do. Procedures for estimation of pollutant loading from watershed and required area for natural systems, and simulation of corresponding reservoir water quality were reviewed. Generally, simulated reservoir water quality was within the reasonable range, and about 15% of total polder farmland was required to meet the agricultural water quality standards. The model was applied based on the current loading condition without additional treatment systems. Wetland system is an ecologically sound treatment system. Therefore, natural systems can be an alternative measure for water quality improvement in polder projects. The area for natural systems was estimated using literature value which might be acceptable at the planning stage. However, pilot system and its experimental data are requisite for large scale field application. WASP5 was proved to be a useful and versatile model, and its application to estuarine reservoir water quality simulation was thought to be appropriate.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.2
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• pp.148-160
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• 2002

Lake water quality assessment information is useful to anyone involved in lake management, from lakeshore owners to lake associations. 11 provides lake water quality, which can improve how to manage lake resources and how to measure current conditions. It also provides a knowledge base that can be used to protect and restore lakes. SWMM was applied to simulate the discharge and pollutant loads from Boryeong watershed, and WASP5 was applied to analyze the changes of water quality in Boryeong freshwater lake. In each model, the most suitable parameters were calculated through sensitive analysis and some parameters used default data. Simulated in SWMM and measured discharge showed the accuracy of 88.6%. T-N and T-P exceeds the criteria in the simulation of water quality in Boryeong freshwater lake, and control of pollutant loads in the main stream showed the most effective way. Integrated water quality management system was developed to give convenience in the operation of SWMM and WASP5 and data acquisition.

• Journal of Korean Society on Water Environment
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• v.20 no.2
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• pp.151-156
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• 2004

A decision support system, Watershed Analysis Risk Management Framework(WARMF), was applied to the Kyungan Stream watershed, a tributary of Lake Paldang, for calculation of total maximum daily loads(TMDL). The WARMF system was developed by Systech Engineering, USA, and has been successfully used in several watersheds, for TMDL studies. The study area was divided into 14 sub-basins, based on digital elevation model(DEM). The integrated watershed and stream model of WARMF was validated by flow and BOD data measured during the year of 1999. There were reasonable agreements between model results and field data, both in water flow and BOD. The validated Kyungan WARMF was extensively utilized to study the quantitative relationship between waste loads and receiving water quality. Based on TMDL guideline at Paldang Lake and Kyungan Stream, the water quality criterion were set to be 3.0mg/L, 3.5mg/L, and 4.0mg/L at the watershed outlet. The allowable waste loads of BOD, both from point and non-point sources, were determined at each water quality criterion. From this study, it was concluded that the WARMF provided several advantages over the conventional application of watershed and stream models for TMDL study, such as time variable simulations, multiple possible soutions, and reduction loads for goal water quality, etc.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Journal of Environmental Impact Assessment
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• v.10 no.3
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• pp.235-243
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• 2001

Since the middle of 1990s, in Korea a few researches on the optimal management technologies combining numerical model and GIS for the management of water environment in drinking watershed area and reservoir such as Paldang Lake have been carried out. In this study, the integrated water environment management system was been suggested to efficiently reflect the public awareness of the environment by integrating the web based distributed data collection system, GIS, public hearing system and water quality model. As all the components of the system have been developed using the World Wide Web and all data have been collected from the relevant agencies through the Internet, the water quality model could be implemented on the web directly. In consequence, the environmental geographic information in Paldang Lake could be acquired and analyzed through the Internet. The system can rapidly respond to the public right to know on environment, so the public will willingly participate in the governmental projects on environment. To verify the usability of the developed system, it has been applied to Paldang Lake. Especially when the web based model has been used, users can easily and confidentially get the prediction results by applying the minimum number of parameters for the water quality model. This model will provide clearness and scientific bases in the process of water quality prediction for the sensitive sites where there are critical conflicts between the residents and the developers. In this study, rapid water environment management technique without spatial and time limit has been suggested, which can contribute to the efforts on the government and the public participation.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.13-22
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• 2007

The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.

• Proceedings of the Korea Water Resources Association Conference
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• 1987.07a
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• pp.159-168
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• 1987

Temporal and spatial prediction of water quality provides the necessary information to a profect planning, design, and model optimization for water quality mangement in a river system. In thes study, the QUAL II model is applied to the Keum River system from the downstream of Dae-Chong dam to the Great Pak-Je bridge. The advection-dispersion model of water quality based on the material balance and the numerical solution method of the model are presented. The enhancement of the model application is empha sized by comparing the observed and the simulated values of BOD, DO, and water temperature. Through these processes, the water quality states of the Keum River system are evaluated and the deoxignation rate, the reaeration rate, and Fair value are estimated. Also, the maintance of the target DO level with the control of the discharge from Dae-Chong dam is discussed.

• Journal of Korea Water Resources Association
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• v.43 no.3
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• pp.283-294
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• 2010

The objective of this study is to develop an accurate and stable dynamic water quality model which is capable of reflecting various flows and irregular cross sections and handling numerical oscillations under the low flow conditions. In order to solve the oscillation problem under the low flow conditions, diffusive wave method was applied to the low flow condition in developing a hydraulic model, DyHYD. DyQUAL is also developed as a water quality model to calculate up to 12 water quality variables including autochthonous BOD, water temperature, DO, TN and TP. The developed model is applied to both hypothetical river channels and actual Nakdong river watershed. Additionally, the applicability and reliability of the models are verified by comparing simulation results with observed values. Nash-Sutcliffe coefficients are estimated by comparison between simulation results and observed values. In the calibration and verification process, the coefficients varies from 0.391 to 0.591 and 0.704 to 0.902 for discharge, BOD, TN and TP, respectively.

• Journal of Korean Society of Water and Wastewater
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• v.9 no.4
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• pp.73-86
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• 1995

The propriety of the numerical model application was examined on Paldang resevoir and its inflow tributaries located in the center of the Korean peninsula and the long term water quality forecast of the oxygen profile was carried out in this syduy. The input data of the model was the capacity of the reservoir, catchment area, percolation, diffusion rate, vertical mixing rate, dissolution rate from the bottom of the reservoir, outflow of the resevoir, water quality measurement and meteorology data of the drainage basin, and the output result was the annual estimation value of the dissolved oxygen concentration and the biochemical oxygen demand. The modeling method is based on the measured or calculated boundary condition dividing the water area into several blocks from the macorscopic aspect and considering the mass balance in these blocks. As the result of the water quality forecast, it was expected that the water quality in Northern Han River and Paldang reservoir would maintain the recent level, but that the water quality in the Southern Han River and its inflow tributary would worsen below the grade 4 of the life environmental standard from around 2000 owing to the decrease of DO concentration and the increase of BOD concentration.