• 대한공간정보학회지
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• 제4권2호
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• pp.23-33
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• 1996

광역수계에서 현재의 수질평가 방법은 시간과 장비 등의 제약으로 오염물질 분포, 이동 및 전반적인 수질현황을 파악하기가 어렵기 때문에 최근에는 대상수역의 수질을 동시적이고 공간적으로 측정을 할 수 있는 원격탐측 적용 연구가 증가추세에 있다. 따라서 본 연구에서는 위성 원격탐측기법으로 호수 수질을 관측하고자 1995년 6월 20일과 1995년 3월 18일에 Landsat 5호 위성의 대청호 상공 통과시간에 맞춰 대청호에서 부영양화 관련 수질인자를 측정하여 위성데이터와 수질 실측치간의 상관관계 분석 및 회귀모델을 유도하였고 모델의 정밀도를 검증하였다 연구결과 TM데이터로부터 수질에 관한 많은 정보를 얻을 수 있었는데, 투명도, 탁도, 부유물질 및 클로로필은 높은 상관성을 보였으나 분광특성이 뚜렷하지 않은 총인, 총질소는 원격탐측 적용이 어려운 것으로 나타났다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.563-567
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• 2004

During drought season, the self-purification capacities of the four major rivers in Korea are significantly controlled by environmental maintenance flows supplied from the mid- or upstream large dams. Therefore, it is obviously important to operate the dams considering not only water quantity aspects but also conservation of downstream water quality and aquatic ecosystems. Mathematical water quality models can be efficiently used to serve as a decision support tool for evaluating the effects of operational alternatives of upstream dams on the downstream aquatic environment. In this study, an unsteady one-dimensional water quality model, KORIV1-WIN was developed based on the theoretical and numerical algorithms for hydrodynamics and water quality simulations of CE-QUAL-RIV1. It consists of hydrodynamic(KORIV1H) and water quality(KORIV1Q) modules, and pre- and post-processors for input data preparations and output displays. The model can be used to predict one-dimensional hydraulic and water quality variations in rivers with highly unsteady flows such as dam outflow change, rainfall-runoff, and chemical spill events.

• 상하수도학회지
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• 제21권5호
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• pp.601-607
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• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.

• Coupled systems mechanics
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• 제11권1호
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• pp.79-91
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• 2022

Models for water treatment processes include simulation, i.e., modelling of water quality, flow hydraulics, process controls and design. Water treatment processes are inherently dynamic because of the large variations in the influent water flow rate, concentration and composition. Moreover, these variations are to a large extent not possible to control. Mathematical models and computer simulations are essential to describe, predict and control the complicated interactions of the water treatment processes. An accurate description of such systems can therefore result in highly complex models, which may not be very useful from a practical, operational point of view. The main objective is to combine knowledge of the process dynamics with mathematical methods for processes estimation and identification. Good modelling practice is way to obtain this objective and to improve water treatment processes(its understanding, design, control and performance- efficiency). By synthesize of existing knowledge and experience on good modelling practices and principles the aim is to help address the critical strategic gaps and weaknessesin water treatment models application.

• Environmental Engineering Research
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• 제16권3호
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• pp.121-129
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• 2011

The completely mixed flow and plug flow (CAP) water quality model was developed for streams with discontinuous flows, a condition that often occurs in low base flow streams with in-stream hydraulic structures, especially during dry seasons. To consider the distinct physical properties of each reach effectively, the CAP model stream network can include both plug flow (PF) segments and completely mixed flow (CMF) segments. Many existing water quality models are capable of simulating various constituents and their interactions in surface water bodies. More complicated models do not necessarily produce more accurate results because of problems in data availability and uncertainties. Due to the complicated and even random nature of environmental forcing functions, it is not possible to construct an ideal model for every situation. Therefore, at present, many governmental level water quality standards and decisions are still based on lumped constituents, such as the carbonaceous biochemical oxygen demand (CBOD), the total nitrogen (TN) or the total phosphorus (TP). In these cases, a model dedicated to predicting the target concentration based on available data may provide as equally accurate results as a general purpose model. The CAP model assumes that its water quality constituents are independent of each other and thus can be applied for any constituent in waters that follow first order reaction kinetics. The CAP model was applied to the Geum River in Korea and tested for CBOD, TN, and TP concentrations. A trial and error method was used for parameter calibration using the field data. The results agreed well with QUAL2EU model predictions.

• 환경영향평가
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• 제15권2호
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• pp.101-109
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• 2006

Water quality simulation models include the difference between the measured and estimated values as an inevitable consequence because they represent the complicated natural phenomena as simplified mathematical equations. The major reason of the difference occurrence is due to the use of the imprecise values of the model parameters, but the parameter values are currently determined by the try and error method directly performed by humans. However, the use of this method requires many time and endeavor of humans, and generally does not obtain the most suitable parameter values. A method for deciding model parameter values is, therefore, developed in this study. The method minimizes the difference between the measured and estimated values and also distributes uniformly the measured values on the upper and lower sides of the line representing the estimated values. A user interface based on this method is also developed by using the Visual Basic 6.0 of Microsoft, and it can be operated in the environment of Windows 98/2000. In this study, the method for deciding model parameter values is applied for estimating the water quality of the stream Ko-heung. The results of the application show that the method, including its computer program, can effectively obtain the most suitable parameter values and also save many working time in comparison with the existing method directly performed by humans.

• 한국수자원학회논문집
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• 제46권7호
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• pp.697-706
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• 2013

최근 한국은 기후변화로 인한 기온 및 수온 상승, 빈번한 집중호우와 친수공간 조성에 따른 적극적인 하천의 활용 등으로 인하여 하천 및 저수지 내 수질관리에 있어 해결해야 하는 많은 문제점을 가지고 있다. 본 연구는 효율적인 수질관리를 위하여 인공신경망을 이용한 단기조류예측모형 구축에 관한 연구이다. 대상지역으로 조류가 번식하기 좋은 조건을 지니고 있는 금강유역 내 대청호를 선정하였고 설치되어 있는 수질 자동측정망의 일 단위자료를 이용하였다. 다층전방향신경망의 역전파 알고리즘을 이용하여 단기(1일, 3일, 7일) 조류를 예측할 수 있는 모형을 구축하였다. 본 모형에서는 대청호 내 수문 및 수질성분을 교차상관분석을 기초하여 단기조류예측모형의 입력 성분을 선정한 후 다양한 조류예측 신경망 모형을 구축하여 결과에 대한 검증을 실시하였다. 구축된 단기조류예측모형은 자연발생적인 기작과 유사한 현상을 재현할 수 있는 다양한 수질인자를 고려하여 단기조류예측모형을 구축한 경우 예측의 정확도가 높게 도출되었다. 본 연구는 신경망모형의 최대 장점인 비선형성 및 간편성 등을 고려하였을 때 우리나라의 수질예측에 적합한 신경망 모형을 구축할 수 있으며 이를 통한 하천 및 호수 내 효율적인 수질관리 방안을 제시할 수 있을 것이다.

• 한국수자원학회논문집
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• 제41권10호
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• pp.1035-1044
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• 2008

하도내에서 발생하는 유출량 및 TOC 자료는 비선형성이 강한 자료임에 따라 홍수에 대한 재난대응과 수질의 상시감시를 위해서는 자료의 특성 분석과 예측에 관한 연구는 필수라 할 수 있다. 따라서 본 연구에서 유출량 및 TOC, TOC부하량 자료에 대한 웨이블렛 변환에 의해 최종분해된 최종파형분해단계의 근사성분과 상세성분을 이용하여 예측모형을 개발하였다. 그 결과 기존 인공신경망 모형에서 관찰되었던 시계반대 방향으로 전이되는 지속현상의 극복 가능성을 보여주었으며, 기존 인공신경망 모형에 비하여 예측의 정확도가 향상됨을 확인할 수 있었다. 이러한 연구결과는 향후 홍수에 대한 피해를 최소화하고 각종 수질사고에 적극적인 대응방안 수립이 가능할 것으로 기대된다.

• 환경영향평가
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• 제20권5호
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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.

• 한국물환경학회지
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• 제21권6호
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• pp.575-583
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• 2005

In this study the flow curve estimation is discussed using TANK model which is one of hydrologic models. The main interest is the accuracy of TANK model parameter estimation with respect to the sampling frequency of input data. For doing this, input data with various sampling frequencies is used to estimate model parameters. As a result, in order to generate relatively accurate flow curve, it is recommendable to measure stream flow at least every 8 days.