• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.73-85
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• 2000

A water quality model was developed for small stream at a upland agricultural watershed. A control volume method was employed to digest the severe variability of stream shape, water quality and discharge at small streams. We estimated optimum reaction coefficients and model structure using a random number generation technique. The index of agreement and coefficient of efficiency were introduced for the model calibration criterion. As the result, the reliability of model parameter estimation could be improved. The applicability of model was tested by a set of sampling results at Yongduckchun in Kimhae. The variability of water quality reaction coefficient was explored through the observed data and using the developed model. model.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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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.

• Journal of Environmental Science International
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• v.8 no.3
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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.

• Journal of Korean Society on Water Environment
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• v.33 no.2
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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.

• Journal of Korea Water Resources Association
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• v.38 no.6 s.155
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• pp.505-515
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• 2005

Two representative river water quality models have been compared in this paper. The steady water quality model, QUAL2E, and the unsteady model, CE-QUAL-RIV1, have been chosen for comparative simulations. Under same reaction coefficients and boundary conditions, the water quality of the Geum river below the Daechung dam has been simulated using two different models, and the water quality equations are compared each other. Since basic model algorithm is very close, the input data required for model run is very similar. Upon the simulation under steady condition, the results of two models show very good agreement especially for BOD, DO, and $NH_3-N$, while the results of specific constituent such as dissolved P is quite different. As a result, dominant water quality parameters to compute each corresponding water quality variables are summarized and tablized through the sensitivity analysis.

• Journal of Environmental Science International
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• v.9 no.6
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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.

• Journal of Environmental Science International
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• v.7 no.4
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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.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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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.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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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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• 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.