• 한국환경보건학회지
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• 제27권3호
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• pp.35-42
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• 2001

The characteristics of water quality variation were predicted by stochastic model in Chungju dam, north Chungcheong province of south Korea, Monthly time series data of water quality from 1989 to 2001;temperature, BOD, COD and SS, were obtained from environmental yearbook and internet homepage of ministry of environment. Development of model was carried out with Box-Jenkins method, which includes model identification, estimation and diagnostic checking. ACF and PACF were used to model identification. AIC and BIC were used to model estimation. Seosonal multiplicative ARIMA(1, 0, 1)(1, 1, 0)$_{12}$ model was appropriate to explain stochastic characteristics of temperature. BOD model was ARMa(2, 2, 1), COD was seasonal multiplicative ARIMA(2. 0. 1)(1. 0, 1)$_{12}$, and SS was ARIMA(1, 0, 2) respectively. The simulated water quality data showed a good fitness to the observed data, as a result of model verification.ion.

• 한국물환경학회지
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• 제28권1호
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• pp.38-49
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• 2012

In this study, we analyze statistical characteristics of influent water quality in Daejeon waste water treatment plant and apply a stochastic model for data generation. In the analysis, the influent water quality data from year 2003 to 2008, except for year 2006, are used. Among water quality variables, we find strong correlations between BOD and T-N; T-N and T-P; BOD and T-P; $COD_{Mn}$ and T-P; and BOD and $COD_{Mn}$. We also find that different water quality variables follow different theoretical probability distribution functions, which also depends on whether the seasonal cycle is removed. Finally, we generate the influent water quality data using the multi-season 1st Markov model (Thomas-Fiering model). With model parameters calibrated for the period 2003~2005, the generated data for 2007~2008 are well compared with observed data showing good agreement in general. BOD and T-N are underestimated by the stochastic model. This is mainly due to the statistical difference in observed data itself between two periods of 2003~2005 and 2007~2008. Therefore, we expect the stochastic model can be applied with more confidence in the case that the data follows stationary pattern.

• 물과 미래
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• 제28권2호
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• pp.103-114
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• 1995

하천 수질변동의 예측을 위한 추계학적 모형 STO-RIV를 개발하였다. STO-RIV는 Streeter-Phelps 확장식의 해석적인 해와 Monte-Carlo 기법으로 구성하였다. 본 모형은 왜관에서 물금에 이르는 낙동강 유역에 적용하여 장래의 물금지점에서의 하천수질의 확률론적 특성이 정량적으로 계산될 수 있었다. 또한 금호강의 처리도 등을 고려한 여러 가지의 수질관리 대안에 대한 수질변동 특성의 해석이 수행되었다. 본 STO-RIV모형은 수질관련변수들의 변동성이 크게 나타나고 있는 국내하천 수질관리에 크게 활용될 수 있을 것으로 기대된다.

• 한국물환경학회지
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• 제24권2호
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• pp.239-246
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• 2008

In this study, an optimized deterministic water-quality model was constructed to estimate water quality of a river and lake in the upstream basin of a dam. A stochastic water-quality analysis using reliability analysis technique was applied to the model. The model was tested in the 13.9 km reach from Maeil stage station of Kyechun to Hoengsung Dam of Sum River. After finding hydraulic characteristics from nonuniform flow analysis, Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization technique for model calibration was applied to determine optimum reaction parameters, and model verification was performed based on these. The stochastic model, using Mean First­Order Second­-Moment (MFOSM) and Monte-Carlo methods, was applied to the same reach as the deterministic study. Variations of discharge and water quality in headwater were considered, as well as variations of hydraulic coefficients and reaction coefficients. The statistical results of output variables from MFOSM were similar to those from the Monte-Carlo method. Risk analysis using MFOSM and Monte-Carlo methods presented the probabilities of some locations in the Hoengsung Lake violating existing water-quality standards in terms of DO and BOD.

• 한국물환경학회지
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• 제20권6호
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• pp.685-691
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• 2004

Water quality forecasting with long term flow is important for management and operation of river environment. However, it is difficult to set up and operate a physical model for water quality forecasting due to large uncertainty in the data required for model setting. Therefore, relatively simpler stochastic approaches are adopted for this problem. In this study we try several multivariate time series models such as ARMAX models for the possible substitute for water quality forecasting. Those models are applied to the BOD and COD levels at Noryangin station, Han river, and also evaluated the effect of release from Paldang dam on them. Monthly BOD and COD data from 1985 to 1991 (7 years) are used for model building and another two year data for model testing. As a result of the study, the effect of improvement on water quality is much more effective combining with the water quality improvement of dam release than considering only increment of dam release in the downstream Han river.

• 한국수자원학회논문집
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• 제35권4호
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• pp.385-395
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• 2002

하천에서의 수질변동을 예측하기 위해 FOEA(First-Order Error-Analysis)와 Monte Carlo 모의를 적용한 추계학적 모형을 개발하였다. 영향메트릭스(Influential matrix)를 이용한 민감도 분석을 실시하여 주요 반응계수를 결정하였고, BFGS(Broyden-Fletcher-Goldfarb-Shanno) 최적화 기법을 사용하여 주요 반응계수 값을 산정하였다. 본 모형을 확정론적 수질해석과 동일한 실제 하도구간에 적용하여 추계학적 수질해석을 실시하였고, 그 결과는 확정론적 해석결과와 잘 일치하였다. 유량과 수질, 반응계수 등에 포함된 불확실도가 하류단의 불확실도에 끼치는 영향을 산정하기 위해 상류단과 지류의 유량 및 수질에 대한 불확실도, 그리고 반응계수의 불확실도에 대한 분석과정이 모형에 포함되었다. 모의수행 결과로부터 각 변수들이 가지고 있는 불확실도가 총 불확실도에 끼치는 영향에 대한 기여도를 산정 할 수 있었다.

• Korean Journal of Hydrosciences
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• 제7권
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• pp.51-60
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• 1996

• 상하수도학회지
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• 제11권3호
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• pp.87-95
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• 1997

A river water quality management model was made by Dynamic programming. This model optimizes the wastewater treatment cost of the application area, and computed water quality with it must meet the water quality standard. And this model takes into consideration tributary input, wastewater treatment plant effluent, withdrawls for several purposes. Modified Streeter-Phelps equation was used to calculate BOD and DO. Optimization problem was solved with particular exceedance probability flow, and the water quality of each point was calculated with the decided treatment efficiencies. At that time, the probability satisfying the water quality standard of constraints to the exceedance probability of the flow. The developed model was applied to the lower part of the Han-River. The reliability to meet the water quality standard is 70 % when 4 wastewater treatment plants of Seoul City are operated by activated sludge system at autumn of the year 2001. Treatment cost of this case is 121.288 billion won per year.

• 물과 미래
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• 제15권3호
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• pp.23-36
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• 1982

The stochastic variations and structures of time series data on water quality were examined by employing the techniques of autocorrelation function, variance spectrum, Fourier series, autoregressive model and ARIMA model. These time series included hourly and daily observation on DO, turbidity, conductivity pH and water temperature. The measurement was made by automatic recording instrument at Noryangjin and Dook-do located in the downstream part of Han River during 1975 and 1976. Hourly water quality time series varied with the dominant 24-hour periodicity, and the 12-hour periodicity was also observed. An important factor affecting 24-hour periodic variation of DO is believed to be photosynthesis by algae. These phenomena might be attributable to periodic discharges of municipal sewage. Noryangjin site showed the more distinct 12-hour periodicity than Dook-do site did, and tidal effect might be responsible for the difference. The water quality, as measured by DO and turbidity, was better in the afternoon compared with the quality in the morning. This change can be explained by the periodic variation of DO, temperature and the amount of municipal wewage discharge. It was also observed that the water temperature at Noryangjin was higher than the temperature at Dook-do. This difference might have been caused by the pollutants that were added to the section between two sites. The correlation coefficients between some of the variables were fairly high. For example, the coefficient was -0.88 between DO and water temperature, 0.75 between turbidity and river flow, and 0.957 between water temperature and air temperature. The lag time of heat transfer from the air to the water was estimated as 24 days. The first order auto-regressive model was appropriate for explaning standardized hourly DO time series. The ARIMA model of (1, 0, 0) type provided relatively satisfactory results for daily DO time series after the removal of significant harmonic value.

• 상하수도학회지
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• 제19권6호
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• pp.738-748
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• 2005

Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.