• 한국수자원학회논문집
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• 제33권1호
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• pp.73-85
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• 2000

농촌 소하천의 수리학적 및 수질특성을 반영한 모형을 개발하였다. 모형구조 설계시 제어체적 기법을 활용하여 하천 형상, 수질 및 유량의 변화가 심한 농촌 유역의 소하천에 대한 수질의 모의하였다. 개발한 모형에 난수발생기법을 도입하여 최적 반응계수와 모형구조를 추정하였다. 또한 모형 보정기준의 일반화를 위해 동의지표와 효율계수를 도입하여 매개변수추정의 신뢰성 향상을 도모했다. 모형의 적용성을 검증하기 위해 경남 김해시 한림면 용덕천에서 수질을 채취하여 분석하였다. 관측된 자료와 개발된 모형의 비교연구를 통해 대상유역의 소하천에서 일어나는 수질 반응계수들과 그 변동성을 추정하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.1400-1403
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• 2004

The current industrial development and the Increase of population along Nakdong River 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, water quality has not significantly improved. The goal of this study is to design a NGIS-based water quality management system for the scientific water quality control and management in the Nakdong River. For general water quality analysis, QULA2E model was applied to the Nakdong River. 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 Nakdong River. A water qualify management system for Nakdong River was made by connecting the QUAL2E model to ArcView. It allows a Windows-based Graphic User Interface(GUI) to implement all operation with regard to water quality analysis. The modeling system in this study will be an efficient NGIS for planning of water quality management.

• 한국환경과학회지
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• 제8권3호
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• pp.355-361
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• 1999

The aim of this study is to develop the water quality simulation model (BAYQUAL) that deal with the physical, chemical and biological aspects of fate/behavior of pollutants in the bay. BAYQUAL is a two dimensional, time-variable finite element water quality model based on the flow simulation model in bay(BAYFLOW). The algorithm is composed of a hydrodynamic module which solves the equations of motion and continuity, a pollutnat dispersion module which solves the dispersion-advection equation. The applicability and feasibility of the model are discussed by applications of the model to the Kwangyang bay of south coastal waters of Korea. Based on the field data, the BAYQUAL model was calibrated and verified. The results were in good agreement with measured value within relative error of 14% for COD, T-N, T-P. Numerical simulations of velocity components and tide amplitude(M2) were agreed closely with the actual data.

• 한국수자원학회논문집
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• 제38권6호
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• pp.505-515
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• 2005

본 논문에서는 두 가지 대표적인 하천수질모형을 비교${\cdot}$분석하였다. 정상상태 모형으로는 QUAL2E, 비정상상태 모형으로는 CE-QUAL-RIV1을 선택하여 동일한 반응계수 및 경계조건 하에서 두 모형의 계산결과를 서로 비교해 보았다. 각 모형에 대하여 수질변동을 계산하기 위해 적용된 방정식을 서로 비교하였으며, 두 가지 수질모형을 이용하여 대청댐 이하 금강본류의 수질을 모의하였다. 두 모형은 기본 알고리듬이 매우 유사하므로, 모형구축에 필요한 입력자료도 매우 비슷하였다. 정상상태로 모의한 경우에 대하여 두 모형의 결과값은 BOD와 DO, 및 $NH_3-N$의 경우에 대하여서는 매우 일치도가 높았다. 그러나 용존인과 같은 특정한 수질인자 항목은 상당한 차이가 있음을 확인하였다. 이러한 모의결과를 바탕으로 두 모형의 민감도 분석을 실시하였으며, 각 수질항목에 대해 지배적으로 작용하는 매개변수를 도표화하여 정리하였다.

• 한국물환경학회지
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• 제33권2호
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• pp.219-229
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• 2017

A watershed-river linked modeling system was developed to forecast the water quality, particularly weekly changes in chlorophyll-a concentration, of the Yeongsan River, Korea. Hydrological Simulation Program-Fortran (HSPF) and Environmental Fluid Dynamics Code (EFDC) were adopted as the basic model framework. In this study, the EFDC model was modified to effectively simulate the operational condition and flow of multi-functional weirs constructed in the main channel of rivers. The model was tested against hydrologic, water quality and algal data collected at the right upstream sites of two weirs in 2014. The mean absolute errors (MAEs) of the model calibration on the annual variations of river stage, TN, TP, and algal concentration are 0.03 ~ 0.10 m, 0.65 ~ 0.67 mg/L, 0.03 ~ 0.04 mg/L, and $9.7{\sim}10.8mg/m^3$, respectively. On the other hand, the MAE values of forecasting results for chlorophyll-a level at the same sites in 2015 range from 18.7 to $22.4mg/m^3$, which are higher than those of model calibration. The increased errors in forecasting are mainly attributed to the higher uncertainties of weather forecasting data compared to the observed data used in model calibration.

• 한국환경과학회지
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• 제9권6호
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• pp.455-462
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• 2000

This study aims at the development of the model for a forecasting of water quality in river basins using artificial neural network technique. Water quality by Artificial Neural Network Model forecasted and compared with observed values at the Sangju q and Dalsung stations in Nakdong river basin. For it, a multi-layer neural network was constructed to forecast river water quality. The neural network learns continuous-valued input and output data. Input data was selected as BOD, CO discharge and precipitation. As a result, it showed that method III of three methods was suitable more han other methods by statistical test(ME, MSE, Bias and VER). Therefore, it showed that Artificial Neural Network Model was suitable for forecasting river water quality.

• 한국환경과학회지
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• 제7권4호
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• pp.433-440
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• 1998

This study was carried out to develop the stream water quality model for the intaking station of Kongju waterworks in the Keum River system. The monthly water quality(total nitrogen and total phosphorus) with periodicity and trend were forecasted by multiplicative ARIU models and then the applicability of the models was tested based on 7 years of the historical monthly water quality data at Kongju intaking strate. The parameter estimation was made with the monthly observed data. The last one year data was used to compare the forecasted water Quality by ARU model with the observed one. The models are ARIMA(2,0,0)$\times$(0,1,1)l2 for total nitrogen, ARIMA(0,1,1)x(0,1,1)l2 for total phosphorus. The forecasting results showed a good agreement with the observed data. It is implying the applicability of multiplicative ARIMA model for forecasting monthly water quality at the Kongju site.

• 한국물환경학회지
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• 제36권6호
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• pp.621-635
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• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.

• 한국물환경학회지
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• 제27권4호
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• pp.413-424
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• 2011

The study was aimed to evaluate the applicability of a three-dimensional (3D) hydrodynamic and water quality model, ELCOM-CAEDYM for Yongdam Reservoir, Korea. The model was applied for the simulations of hydrodynamics, thermal stratification processes, stream density flow propagation, and water quality parameters including dissolved oxygen, nutrients, organic materials, and algal biomass (chl-a) for the period of June to December, 2006. The field data observed at four monitoring stations (ST1~ST4) within the reservoir were used to validate the models performance. The model showed reasonable performance nevertheless low frequency boundary forcing data were provided, and well replicated the physical, chemical, and biological processes of the system. Simulated spatial and temporal variations of water temperature, nutrients, and chl-a concentrations were moderately consistent with the field observations. In particular, the model rationally reproduced the succession of different algal species; i.e., diatom dominant during spring and early summer, after then cyanobacteria dominant under warm and stratified conditions. ELCOM-CAEDYM is recommendable as a suitable coupled 3D hydrodynamic and water quality model that can be effectively used for the advanced water quality management of large stratified reservoirs in Korea.

• Water Engineering Research
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• 제3권2호
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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.