• 상하수도학회지
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• 제21권3호
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• pp.305-314
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• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model

• 한국농공학회지
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• 제32권3호
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• pp.116-129
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• 1990

A rational management of water resources in estuary reservoirs necessiates the prediction of water quality. In this study, a multiple box model for the water quality prediction was developed as a tool for the purpose of examining an adequate way to improve and maintain the water quality. Some submodels that are suitable for simulating the mixing behavior of pollutant materials in a lake were considered in this model. The model was appiled for predicting water qualities of Haenam Esturay Reservoir. The result from this study can be summarized as follows : 1.A water quality simulation model that can predict the 10-day mean value of water qualities was developed by adding some submodels that simulate the concentrations of chlorophyll-a, BOD, T-P and T-N to the existing Multiple Box Model representing the mixing and circulating of materials by the hydarulic action. 2.As input data for the model developed, the climatic data including precipitation, solar radiation, temperature, cloudness, wind speed and relative humidity, and the water buget records including the pumping discharge and the releasing discharge by drainage gate were ollected. The hydrologic data for the inflow discharge from the watershed was obtained by simulation with the aid of USDAUL-74/SNUA watershed model. Also the water quality data were measured at streams and the reservoir. 3.As a result of calibration and verification test by using four comonents of water quality such as Chlorophyll-a, BOD, T-P and T-N, it was found that the correlation coefficeints between the observed and the simulated water qualities showed greater than 0.6, therefore the capability of the model to simulate the water quality was proved. 4.The result based on the model application showed that the water quality of the Haenam Estuary Reservoir varies seasonally with the harmonic trend, however the water quality is good in winter and get worse in summer. Also it may be concluded that the current grarde of water quality in the Heanam Esutary Reservoir is ranked as grade 4 suitable only for the agricultutal use.

• 한국수자원학회논문집
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• 제44권6호
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• pp.477-486
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• 2011

본 연구는 인공수로 또는 인공호수와 같은 환경 친화적 친수공간이 건설됨에 따라 발생되는 수질오염 문제를 수질모형실험을 통해 해소할 수 있는 방법을 연구하였다. 호소수의 개념으로 도입되는 인공수로 및 호수는 제한된 수량 공급으로 인해 수질악화, 악취발생 및 녹조 현상이 일어날 수 있다. 하지만, 이러한 현상을 예상할 수 있는 방법은 수치해석으로만 의지해 왔고 물리적 해석에 의한 검증이 이뤄지지 않아 실제 적용에 대한 어려움이 있었다. 따라서 본 연구에서는 오염된 물과 오염되지 않은 물이 서로 희석되어 영양염류의 농도를 낮추는 현상에 착안하여 원형과 모형의 상사에 영향을 받지 않는 무차원의 물리적 수질모형실험을 실시하였다. 또한 수체 내에서 흐름을 인공적으로 발생시키는 흐름유발기기의 효과 검증과 적용방법을 연구하여 목표수질을 유지할 수 있는 방안을 제시하였다.

• 한국물환경학회지
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• 제20권4호
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• pp.321-326
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• 2004

The objective of this study is to test the applicability of neural network models to forecast water quality at Naesa and Pyongchang river. Water quality data devided into rainy day and non-rainy day to find characteristics of them. The mean and maximum data of rainy day show higher than those of non-rainy day. And discharge correlate with TOC at Pyongchang river. Neural network model is trained to the correlation of discharge with water quality. As a result, it is convinced that the proposed neural network model can apply to the analysis of real time water quality monitoring.

• Ecology and Resilient Infrastructure
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• 제9권1호
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• pp.24-35
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• 2022

수질분야에서 물재해 안정성 강화를 위해 과거와 현재의 수질을 분석하여 예측하는 기술을 지속적으로 고도화하는 것이 필요하며 데이터 기반의 예측 모형이 하나의 대안으로 대두되고 있다. 데이터 기반 모형은 복잡하고 광범위한 자료의 양을 기반으로 구축되기 때문에 보다 신뢰도 있는 결과를 얻을 수 있는 입력자료의 조합을 위한 상관관계 분석방법의 적용이 필수적이다. 본 연구에서는 보다 신속하고 정확한 데이터 기반의 수질 예측 모형을 구성하기 위한 선행단계로 Gamma Test를 적용하였다. 먼저 팔당댐의 다양한 수문조건에 따른 해당 유역의 복잡성과 정밀성이 재현된 과거와 현재의 일단위 수질을 최대한 확보하고자 물리적 기반 모형 (HSPF, EFDC)을 구동하였다. 팔당댐 수질예측지점과 팔당댐으로 유입되는 주요 하천의 수질을 대상으로 Gamma Test를 수행한 후 해석결과 (Gamma, Gradient, Standar Error, V-Ratio)를 통해 최적의 자료조합을 선정하는 방법을 제시하였다. 본 연구의 결과는 데이터 기반 모형 구축 시 반복적인 수행과정을 생략하여 시간을 단축하면서 보다 효율적으로 최적의 입력자료를 선정할 수 있는 정량적인 기준을 보여준다.

• 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.

• 한국물환경학회지
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• 제25권6호
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• pp.902-909
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• 2009

With various reservoirs, dams and reduction of water velocity in downstream, rivers in Korea often have characteristics of accumulation of pollutants. Therefore, the main focus of water quality modeling in Korea needs to be shifted from DO to algae and organic matter. Moreover the structures of water quality models should be modified to have capability of simulating BOD which is a key factor of total water pollution load management in Korea as laboratory experiment BOD (Bottle $BOD_5$). In the SWAT model which is one of the widely used water quality models in Korea, the channel water quality module is using main algorithm of the QUAL2E model which has limitations in simulating algae, organic matter and Bottle BOD5 etc. To overcome this hindrance, in this study, the improved channel water quality module of the SWAT model (Q-SWAT) was proposed by linking the algorithms of the QUAL-NIER model which was developed based on the QUAL2E model to the SWAT model. The algorithms estimating the increase of internal organic matter by fractionization algal metabolism process and calculating Bottle $BOD_5$ were added and the results of proposed model were compared to those of the original SWAT model. The results of comparison test are showing that more accurate BOD values can be obtained with the Q-SWAT model and it is anticipated that the Q-SWAT model can be used as an effective tool of decision support through the water quality simulation and long term pollution source analysis.

• 한국수자원학회논문집
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• 제44권6호
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• pp.439-447
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• 2011

This study aims to test the feasibility of combined use of EFDC (Environmental Fluid Dynamics Code) hydrodynamic model and WASP7.3 (Water Quality Analysis Program) model to improve accuracy of water quality predictions of the Yongdam Lake, Korea. The orthogonal curvilinear grid system was used for EFDC model to represent riverine shape of the study area. Relationship between volume, surface and elevation results were checked to verify if the grid system represents morphology of the lake properly. Monthly average boundary water quality conditions were estimated using the monthly monitored water quality data from Korean Ministry of Environment DB system. Monthly tributary flow rates were back-routed using dam discharge data and allocated in proportion to each basin area as direct measurements were not available. The optimum number of grid system was determined to be 372 horizontal cells and 10 vertical layers of the site for 1 year simulation of hydrodynamics and water quality out of iterative trials. Monthly observed BOD, TN, TP and Chl-a concentrations inside the lake were used for calibration of WASP7.3 model. This study shows that EFDC and WASP can be used in series successfully to improve accuracy in water quality modeling. However, it was observed that the amount of data to develop inflow water quality and flow rate boundary conditions and water quality data inside lake for calibration were not enough for accurate modeling. It is suggested that object-oriented data collection systems would be necessary to ensure accuracy of EFDC-WASP model application and thus for efficient lake water quality management strategy development.

• 물과 미래
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• 제29권2호
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• pp.179-190
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• 1996

한강하류부 수질의 통계학적 해석을 통하여 수질 시계열자료의 기본 통계특성치, 지점별 및 계절별 변동성을 검토하였으며, 유량과 수질인자간의 상관성 분석을 실시하였다. 본류의 주요 6개 지점 및 3개 지류에 대한 통계특성치와 적정분포형을 산정하여 제시하였으며, 시간의존성 및 계절성을 검토하여 제시하였다. 또한, 수질 항목간의 상관성 검토를 통하여 상관성이 높은 수질, 항목간, 그리고 지점간의 상관식을 제시하였다. 추계학적 모의모형의 적용가능성을 확인하였으며, DO 항목은 전 지점간에 높은 상관성을 가지고 있었다. 유량과의 상관관계 검토에 있어서 DO, SS 항목은 유량보다는 수온에 민감하였으며, BOD, COD 항목은 유량이 적은 갈수기에는 유량에 민감한 것으로 나타났다. 수온에 밀접한 영향을 받는 DO 항목외에도 BOD, COD 항목은 계절적인 주기성을 가지고 있었으며, 상호상관 분석결과 DO, BOD, COD 항목 외의 수질 항목들에서도 각 수질 항목들에 내재된 주기성을 찾아볼 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.282-285
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• 1996

In the water purification plant, the raw water is promptly purified by injecting chemicals. The amount of chemicals is directly related to water quality such as turbidity, temperature, pH and alkalinity. At present, however, the process of chemical reaction to the turbidity has not been clarified as yet. Since the process of coagulant dosage has no feedback signal, the amount of chemical can not be calculated from water quality data which were sensed from the plant. Accordingly, it has to be judged and determined by Jar-Test data which were made by skilled operators. In this paper, it is concerned to model and control the coagulant dosing process using jar-test results in order to predict optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride). The considering relations to the reaction of coagulation and flocculation, the five independent variables(turbidity, temperature, pH, Alkalinity of the raw water, PAC feed rate) are selected out and they are put into calculation to develope a neural network model and a fuzzy model for coagulant dosing process in water purification system. These model are utilized to predict optimum coagulant dosage which can minimize the water turbidity in flocculator. The efficacy of the proposed control schemes was examined by the field test.