• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.765-777
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• 2011

According to the total pollution load management system, exact prediction and analysis of water quality and discharge has been required in order to allocate the amount of pollution load to each local government. In this study, QUAL2E model was used for comparison with other water quality models and improve the inadequate to forecast future water quality. And Various calibration and verification methods were applied to deal with existing uncertainties of parameter during modeling water quality. For user convenience, A GUI(Graphical User Interface) system named "QL2-XP" model is developed by object-oriented language for the user convenience and practical usage. Suggested GUI system consist of hydraulic analysis, water quality analysis, optimized model calibration processes, and postprocessing the simulation results. Therefore this model will be effectively utilized to manage practical and efficient water quality.

• Journal of Ocean Engineering and Technology
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• v.22 no.5
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• pp.75-82
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• 2008

As a basic study for establishing a countermeasure for an oxygen deficient water mass (ODW), we investigated the variation of ODW volume according to the enforced total pollution load management in Jinhae Bay. This study estimated the inflowing pollutant loads into Jinhae Bay and predicted the reduction in ODW by using a sediment-water ecological model (SWEM). The result obtained in this study are summarized as follows: 1) The daily average pollutant loads of COD, SS, TN, TP, DIN, and DIP inflowing into Jinhae bay in 2005 were estimated to be about 12,218 kg-COD/day, 91,884 kg-SS/day, 5,292 kg-TN/day, 182 kg-TP/day, 4,236 kg-DIN/day, and 130 kg-DIP/day. 2) The calculated results of the tidal current by the hydrodynamic model showed good agreement with the observed currents. Also, an ecological model well reproduced the spatial distribution of the water quality in the bay. 3) This study defined the ODWDI (ODW decreasing index) in order to estimate the ODW decreasing volume caused by a reduction in the inflowing pollutant loads. As a result, the ODWDI was predicted to be about 0.91 (COD 30% reduction), 0.87 (COD 50% reduction), 0.79 (COD 70% reduction), 0.85 (ALL 30% reduction), 0.66 (ALL 50% reduction), and 0.45 (ALL 70% reduction). The ODW volume was decreased 1.5 $\sim$ 2.6 times with a reduction in the COD, TN, and TP inflowing pollutant loads compared to a reduction in just the COD inflowing pollutant load. Therefore, it is necessary to enforce total pollution load management, not only for COD, but also fm TN and TP.

• 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.

• Korean Journal of Environmental Agriculture
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• v.19 no.3
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• pp.252-258
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• 2000

SWMM and WASP5 were applied for pollutant loading estimate from watershed and reservoir water quality simulation, respectively, to predict estuarine reservoir water quality. Application of natural systems to improve estuarine reservoir water quality was reviewed, and its effect was predicted by WASP5. Study area was the Hwa-Ong reservoir in Hwasung-Gun, Kyonggi-Do. Procedures for estimation of pollutant loading from watershed and simulation of corresponding reservoir water quality were reviewed. In this study, SWMM was proved to be an appropriate watershed model to the nonurban area, and it could evaluate land use effects and many hydrological characteristics of catchment. WASP5 is a well known lake water quality model and its application to the estuarine reservoir was proved to be suitable. These models are both dynamic and the output of SWMM can be linked to the WASP5 with little effort, therefore, use of these models for reservoir water quality prediction in connection was appropriate. Further efforts to develop more logical and practical measures to predict reservoir water quality are necessary for proper management of estuarine reservoirs.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.182-192
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• 2008

In oder to develop a decision supporting system for oil spill response, the prototype of pollution response support system which has integrated oil spill prediction system and pollution risk prediction system has developed for Incheon-Daesan area. Spill prediction system calculates oil spill aspects based on real-time wind data and real-time water flow and the residual volume of spilt oil and spread pattern are calculated considering the characteristic of spilt oil. In this study, real-time data is created from results of real-time meteorological forecasting model(National Institute of Environmental Research) using ftp, real-time tidal currents datasets are built using CHARRY(Current by Harmonic Response to the Reference Yardstick) model and real-time wind-driven currents are calculated applying the correlation function between wind and wind-driven currents. In order to model the feature which is spilt oil spreading according to real-time water flow is weathered, the decrease ratio by oil kinds was used. These real-time data and real-time prediction information have been integrated with ESI(Environmental Sensitivity Index) and response resources and then these are provided using GIS as a whole system to make the response strategy.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1387-1391
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• 2007

In this study, water quality prediction that is necessary to water quality forecasting system is performed using 2-D river analysis models RMA-2 and RAM4. RAM4 is suitable to water quality forecasting system cause it is possible to put in the pollutants as a mass type boundary condition. Instant injections of pollutants at Yongdamdaegyo Bridge in Namhangang River are simulated and the behavior of pollutant cloud is observed. The effects of water quality accident to Paldang 2 water intake plants in Paldangho Lake is analyzed with time variation. And extra flow simulation is performed for mitigation of pollution. Several cases of water quality forecasting system at home and abroad are investigated and the direction of water quality forecasting system is presented.

• Journal of Korean Society on Water Environment
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• v.21 no.6
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• pp.617-623
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• 2005

Forestry and agricultural land uses constitute 85% of Korea and these land uses are typically mixed in many watersheds. Biological Oxygen Demand (BOD) concentration is a primary factor for managing water qualities of the water resources in Korea. BOD loadings from diffuse sources, however, not well monitored yet. This study aims to assess BOD loadings from diffuse sources and their affecting factors to conserve quality of water resources. Event Mean Concentration (EMC) of BOD was calculated based on the monitoring data of forty rainfall events at four agricultural-forestry watersheds. Exceedence cumulative probability of BOD EMCs were plotted to show agricultural activities in a watershed impacts on the magnitude of EMCs. Prediction equation for each rainfall event was proposed to estimate BOD EMCs: $EMC_{BOD}(mg/L)=EXP(0.413+0.0000001157{\times}$(discharged runoff volume in $m^3$)+0.018${\times}$(ratio of agricultural land use to total watershed area).

• Journal of Korean Society on Water Environment
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• v.32 no.6
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• pp.589-599
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• 2016

Implemented since 2004, TPLC (Total Pollution Load Control) is the most powerful water-quality protection program. Recently, uncertainty of prediction using steady state model increased due to changing water environments, and necessity of a dynamic state model, especially the watershed model, gained importance. For application of watershed model on TPLC, it needs to be feasible to adjust the relationship (mass-balance) between discharged loads estimated by technical guidance, and arrived loads based on observed data at the watershed outlet. However, at HSPF, simulation is performed as a semi-distributed model (lumped model) in a sub-basin. Therefore, if the estimated discharged loads from individual pollution source is directly entered as the point source data into the RCHRES module (without delivery ratio), the pollutant load is not reduced properly until it reaches the outlet of the sub-basin. The hypothetic RCHRES generated using the HSPF BMP Reach Toolkit was applied to solve this problem (although this is not the original application of Reach Toolkit). It was observed that the impact of discharged load according to spatial distribution of pollution sources in a sub-basin, could be expressed by multi-segmentation of the hypothetical RCHRES. Thus, the discharged pollutant load could be adjusted easily by modification of the infiltration rate or characteristics of flow control devices.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.1-8
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• 2023

Monitoring and prediction of water quality are essential for effective river pollution prevention and water quality management. In this study, a multi-classification model was developed to predict chlorophyll-a (Chl-a) level in rivers. A model was developed using CatBoost, a novel ensemble machine learning algorithm. The model was developed using hourly field monitoring data collected from January 1 to December 31, 2015. For model development, chl-a was classified into class 1 (Chl-a≤10 ㎍/L), class 2 (10<Chl-a≤50 ㎍/L), and class 3 (Chl-a>50 ㎍/L), where the number of data used for the model training were 27,192, 11,031, and 511, respectively. The macro averages of precision, recall, and F1-score for the three classes were 0.58, 0.58, and 0.58, respectively, while the weighted averages were 0.89, 0.90, and 0.89, for precision, recall, and F1-score, respectively. The model showed relatively poor performance for class 3 where the number of observations was much smaller compared to the other two classes. The imbalance of data distribution among the three classes was resolved by using the synthetic minority over-sampling technique (SMOTE) algorithm, where the number of data used for model training was evenly distributed as 26,868 for each class. The model performance was improved with the macro averages of precision, rcall, and F1-score of the three classes as 0.58, 0.70, and 0.59, respectively, while the weighted averages were 0.88, 0.84, and 0.86 after SMOTE application.