• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.2
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• pp.116-125
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• 2003

Predictions of stream water quality require both estimation of pollutant loading from different sources and simulation of water quality processes in the stream. Nonpoint source pollution models are often employed for estimating pollutant loading in rural watersheds. In this study, a conjunctive application of SWAT model and WASP model was made and evaluated for its applicability based on the simulation results. Runoff and nutrient loading obtained from the SWAT model were used for generating input data for WASP model. The results showed that the simulated runoff was in good agreement with the observed data and indicated reasonable applicability. Loading for the water quality parameters predicted by WASP model also showed a reasonable agreement with the observed data. It is expected that stream water quality could be predicted by the coupled application of the two models, SWAT and WASP, in rural watersheds.

• Journal of Industrial Technology
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• v.18
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• pp.443-452
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• 1998

The future water quality of Youngwol Dam was predicted using FEMWASP. In the this study, point and non-point source in the basin was investigated in detail, and future pollutant loading was computed by various prediction technique. The water quality of 29 sites was analyzed over four seasons. FEMWASP was used to predict future water quality of Youngwol lake and downstream of proposed dam. Future water quality of Youngwol lake was predicted to configure eutrophication status, management criteria was suggested to minimize the pollution problems coming from future eutrophication. Discharge rate of dam was decided as 30CMS to conserve the water quality, and overall design of dam was changed.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.51-62
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• 2017

Since living environment has improved, waterfront space using and clear water demand have increased. Ministry of Environment (ME) designated polluted reservoir (worse than 4th grade) as a priority management reservoir to improve water quality (better than 3rd grade) accordingly. Minstry of Agriculture, Food and Rural Affairs (MAFRA) aims reservoir water quality 4th not 3rd grade. And water quality of agricultural reservoirs was not a great interest. For this reason, there are very few water quality monitoring data. However after designating as a priority management reservoir, reservoir manager should start water quality and flow monitoring of reservoirs and inflow streams. This process makes it possible setting complex model to accurate prediction of reservoir water quality and volume. Mulwang reservoir designated as a priority management reservoir in September 2014. In this study, BASINS/WinHSPF and EFDC-WASP were used to predict effect of water quality improvement countermeasures in Mulwang reservoir. To improve water quality of Mulwang reservoir, Siheung-si and Korea Rural Community Corporation (KRCC) established water quality improvement countermeasures. However result of simulation adapting these countermeasures cannot achieve 3rd grade. So 4 additional scenarios were adapted and the result satisfied 3rd grade. This study could help to establish water quality improvement countermeasure by using complex modeling.

• 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.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.5
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• pp.96-105
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• 2002

As an useful water purification system for non-point source pollution in rural watersheds, interests in constructed wetlands are growing at home and abroad. It is well known that constructed wetlands are easily installed, no special managemental needs, and more flexible at fluctuating influent loads. They have a capacity for purification against nutrient materials such as phosphorus and nitrogen causing eutrophication of lentic water bodies. The Constructed Wetland Design Model (CWDM), developed through this study is consisted mainly of Database System, Runoff-discharge Prediction Submodel, Water Quality Prediction Submodel, and Area Assessment Submodel. The Database System includes data of watershed, discharge, water quality, pollution source, and design factors for the constructed wetland. It supplies data when predicting water quality and calculating the required areas of constructed wetlands. For the assessment of design flow, the GWLF (Generalized Watershed Loading Function) is used, and for water quality prediction in streams estimating influent pollutant load, Water Quality Prediction Submodel, that is a submodel of DSS-WQMRA model developed by previous works is amended. The calculation of the required areas of constructed wetlands is achieved using effluent target concentrations and area calculation equations that developed from the monitoring results in the United States. The CWDM is applied to Bokha watershed to appraise its application by assessing design flow and predicting water quality. Its application is performed through two calculations: one is to achieve each target effluent concentrations of BOD, SS, T-N and T-P, the other is to achieve overall target effluent concentrations. To prove the validity of the model, a comparison of unit removal rates between the calculated one from this study and the monitoring result from existing wetlands in Korea, Japan and United States was made. As a result, the CWDM could be very useful design tool for the constructed wetland in rural watersheds and for the non-point source pollution management.

• Journal of the Korea Society of Computer and Information
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• v.21 no.8
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• pp.57-64
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• 2016

Initial prevention activities and rapid propagation conditions is the most important to prevent diffusion of water pollution. If water pollutants flow into streams river or main stresm located in environmental conservation area or water intake facilities, we must predict immediately arrival time and the diffusion concentration to the proactive. National Institute of Environmental Research developed water pollution incident response prediction system linking dam and movable weir. the system is mathematical model which is updated daily. Therefore it can quickly predict the arrival time and the diffusion concentration when there are accident of oil spills and hazardous chemicals. Also we equipped with mathematical model and toxicity model of EFDC(Environmental Fluid Dynamics Code) to calculate the arrival time and the diffusion concentration. However these systems offer the services of an offline manner than real-time control services. we have ensured the reliability of data collection and have developed a real-time water quality measurement data transmission device by using the data linkage utilizing a mode bus communication and a commercial SCADA system, in particular, we implemented to be able to do real-time water quality prediction through information infrastructure of the water quality integrated management business created by utilizing the construction of the real-time prediction system that utilizes the data collected, the Open map, the visual representation using charts API and development of integrated management system development based on web maps.

• Journal of Environmental Impact Assessment
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• v.9 no.1
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• pp.13-24
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• 2000

This paper presents a study of water quality model applications in environmental impact statements which were submitted during recent years in Korea. Most of the applications have reported that the development projects would have significant impacts on the water quality, especially, of streams and rivers. The water quality models, however, were hardly used as an impact prediction tool. Even in the cases where models were used, calibration and verification studies were not performed and thus the predicted results would not be reliable. These poor model applications in environmental impact assessment can be attributable to the fact that there were no available model application guidelines as well as no requirements by the review agency. In addition, the expected waste loads were improperly estimated in most cases, especially in non-point sources, and the predicted parameters were not good enough to understand water quality problems expected from the proposed plans. The effects of mitigation measures were not analyzed in most cases. Again, these can be attributed to no formal guidelines available for impact predictions until now. A brief guideline is described in this paper, including model selection, calibration and verification, impact prediction, and analysis of effects of mitigation measures. The results of this study indicate that the model application should be required to overcome the current improper predictions of environmental impacts and the guidelines should be developed in detail and provided.

• Journal of Environmental Science International
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• v.13 no.11
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• pp.993-1000
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• 2004

As an effort to clarify the ecosystem of Osaka Bay, a semi-enclosed coastal area under the influence of stratification, a three-dimensional water quality model with combination of the baroclinic flow model and primitive eco-system model was constructed. The proposed model succeeded in simulating the time-depending flow and density structure and the baroclinic residual currents in Osaka Bay. In present study, we tried to improve the model by taking account of the benthic-pelagic interaction and exchange of nutrients between sea bottom sediments and overlaying water. On vertical structure, the model consists of 13 layers of water and eight layers of sediments. Long-term prediction of water quality was conducted from 1964 to 1985. This period is characterized by rapid water pollution and its decrease by the cutoff reduction of COD and P flowed into Osaka Bay. By combining the sediment model into original model, the numerical model was confirmed to shows more reasonable results in simulating the water quality in Osaka Bay.

• Membrane and Water Treatment
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• v.10 no.1
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• pp.1-11
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• 2019

The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) predicted that recent extreme hydrological events would affect water quality and aggravate various forms of water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed and sunlight) were established using the Representative Concentration Pathways (RCP) 8.5 climate change scenario suggested by the AR5 and calculated the future runoff for each target period (Reference:1989-2015; I: 2016-2040; II: 2041-2070; and III: 2071-2099) using the semi-distributed land use-based runoff processes (SLURP) model. Meteorological factors that affect water quality (precipitation, temperature and runoff) were inputted into the multiple linear regression analysis (MLRA) and artificial neural network (ANN) models to analyze water quality data, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N) and total phosphorus (T-P). Future water quality prediction of the Anseongcheon River basin shows that DO at Gongdo station in the river will drop by 35% in autumn by the end of the $21^{st}$ century and that BOD, COD and SS will increase by 36%, 20% and 42%, respectively. Analysis revealed that the oxygen demand at Dongyeongyo station will decrease by 17% in summer and BOD, COD and SS will increase by 30%, 12% and 17%, respectively. This study suggests that there is a need to continuously monitor the water quality of the Anseongcheon River basin for long-term management. A more reliable prediction of future water quality will be achieved if various social scenarios and climate data are taken into consideration.