• 상하수도학회지
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• 제14권2호
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• pp.174-180
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• 2000

In this study two water quality models, widely used in Korea, WASP5 and SWRRB were applied to Lake Paldang. The simulated results were compared with the measured data. The simulation results using WASP5 showed that this model could reasonably predict the concentrations of $NO_3$-N, Organic N, and Organic P. In order to investigate the effect of pollution by non-point source SWRRB was simulated and the concentrations of nutrients were predicted. The results from WASP5 and SWRRB are not directly comparable because their input data are different and output values are differently presented. Therefore, if these two simulation models were applied simultaneously, many valuable data and information could be obtained due to their own applicabilities and advantages.

• 한국농공학회논문집
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• 제57권2호
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• pp.37-45
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• 2015

Typically, water quality sampling takes place intermittently since sample collection and following analysis requires substantial cost and efforts. Therefore regression models (or rating curves) are often used to interpolate water quality data. LOADEST has nine regression models to estimate water quality data, and one regression model needs to be selected automatically or manually. The nine regression models in LOADEST and auto-selection by LOADEST were evaluated in the study. Suspended solids data were collected from forty-nine stations from the Water Information System of the Ministry of Environment. Suspended solid data from each station was divided into two groups for calibration and validation. Nash-Stucliffe efficiency (NSE) and coefficient of determination ($R_2$) were used to evaluate estimated suspended solid loads. The regression models numbered 1 and 3 in LOADEST provided higher NSE and $R_2$, compared to the other regression models. The regression modes numbered 2, 5, 6, 8, and 9 in LOADEST provided low NSE. In addition, the regression model selected by LOADEST did not necessarily provide better suspended solid estimations than the other regression models did.

• 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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회(1)
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• pp.47-49
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• 2005

• 생태와환경
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• 제46권1호
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• pp.1-9
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• 2013

Algal blooms in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. In addition to precipitating taste and odor problems, blooms damage the environment, and some classes like cyanobacteria (blue-green algae) release toxins that can threaten human health, even causing death. There is a recognized need in the water industry for models that can accurately forecast in real-time algal bloom events for planning and mitigation purposes. In this study, using data for an interconnected system of rivers and reservoirs operated by a New Jersey water utility, various ANN models, including both discrete prediction and classification models, were developed and tested for forecasting counts of three different algal classes for one-week and two-weeks ahead periods. Predictor model inputs included physical, meteorological, chemical, and biological variables, and two different temporal schemes for processing inputs relative to the prediction event were used. Despite relatively limited historical data, the discrete prediction ANN models generally performed well during validation, achieving relatively high correlation coefficients, and often predicting the formation and dissipation of high algae count periods. The ANN classification models also performed well, with average classification percentages averaging 94 percent accuracy. Despite relatively limited data events, this study demonstrates that with adequate data collection, both in terms of the number of historical events and availability of important predictor variables, ANNs can provide accurate real-time forecasts of algal population counts, as well as foster increased understanding of important cause and effect relationships, which can be used to both improve monitoring programs and forecasting efforts.

• Korean Journal of Hydrosciences
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• 제10권
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• pp.35-46
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• 1999

An integrated system of GIS and water quality model was suggested including the pollutant loads from the watershed. The developed system consits of two parts. First part is the information on landuse and several surface factors concerning the overland flow processes of water and pollutants. Second part is the modeling modules which include storm event pollutant load model(SEPLM), non-storm event pollutant load model(NSPLM), and river water quality simulation model(RWQSM). Models can calculate the pollutant load from the study area. The databases and models are linked through the interface modules resided in the overall system, which incorporate the graphical display modules and the operating scheme for the optimal use of the system. The developed system was applied to the Chungju multi-purpose reservoir to estimate the pollutant load during the four selected rainfall events between 1991 and 1993, based upon monthly basis and seasonal basis in drought flow, low flow, normal flow and wet flow.

• 한국물환경학회지
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• 제30권1호
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• pp.16-24
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• 2014

A combined watershed and receiving waterbody model was developed for operational water flow forecasting of the Nakdong river. The Hydrological Simulation Program Fortran (HSPF) was used for simulating the flow rates at major tributaries. To simulate the flow dynamics in the main stream, a three-dimensional hydrodynamic model, EFDC was used with the inputs derived from the HSPF simulation. The combined models were calibrated and verified using the data measured under different hydrometeological and hydraulic conditions. The model results were generally in good agreement with the field measurements in both calibration and verification. The 7-days forecasting performance of water flows in the Nakdong river was satisfying compared with model calibration results. The forecasting results suggested that the water flow forecasting errors were primarily attributed to the uncertainties of the models, numerical weather prediction, and water release at the hydraulic structures such as upstream dams and weirs. From the results, it is concluded that the combined watershed-waterbody model could successfully simulate the water flows in the Nakdong river. Also, it is suggested that integrating real-time data and information of dam/weir operation plans into model simulation would be essential to improve forecasting reliability.

• 한국수자원학회논문집
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• 제38권7호
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• pp.565-574
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• 2005

평창강 수질자동측정망 실시간 자료를 이용하여 강우시와 무강우시로 구분하여 분석하였다. 강우시에 측정된 TOC 자료는 무강우시 측정된 자료에 비해 평균값, 최대값, 표준편차가 크게 나타났으며, 강우시의 DO 자료는 무강우시에 측정된 자료보다 낮아 유량이 수질변화에 영향을 미치는 것으로 분석되었다. 신경망 모형과 뉴로-퍼지 모형으로 수질예측 모형을 구성하고, 적용하였다. LMNN, MDNN, ANFIS 모형은 TOC 모의에서 DO 예측에서는 LMNN, MDNN 모형이 ANFIS 모형보다 좋은 결과를 보였으며, 정량적 자료에 정성적 자료인 시간을 학습한 MDNN 모형이 가장 작은 오차를 보였다. 하천의 실시간적 관리를 위해서는 유량과 수질의 측정이 동일한 지점에서 동시간적으로 이루어져야 보다 효과적이다. 그러나 수질자동측정망 지점과 T/M 수위관측소가 원거리에 위치한 경우들이 있으며, 평창강 수질자동측정망 지점이 그 중 하나이다. 연구에서는 평창강 수질자동측정망 지점의 유출예측을 위한 신경망 모형을 구성하여 수질예측 모형과 연계하였으며, 연계된 모형은 수질예측에 개선된 결과를 보였다.

• 한국농공학회지
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• 제44권2호
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• pp.148-160
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• 2002

Lake water quality assessment information is useful to anyone involved in lake management, from lakeshore owners to lake associations. 11 provides lake water quality, which can improve how to manage lake resources and how to measure current conditions. It also provides a knowledge base that can be used to protect and restore lakes. SWMM was applied to simulate the discharge and pollutant loads from Boryeong watershed, and WASP5 was applied to analyze the changes of water quality in Boryeong freshwater lake. In each model, the most suitable parameters were calculated through sensitive analysis and some parameters used default data. Simulated in SWMM and measured discharge showed the accuracy of 88.6%. T-N and T-P exceeds the criteria in the simulation of water quality in Boryeong freshwater lake, and control of pollutant loads in the main stream showed the most effective way. Integrated water quality management system was developed to give convenience in the operation of SWMM and WASP5 and data acquisition.

• 한국측량학회지
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• 제19권3호
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• pp.253-261
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• 2001

현재 하천의 수질관리를 위하여 여러 수질모델이 개발되어 있으며, 이러한 수질예측모델에 각종 수질관리에 따른 대안을 적용시킴으로써 그 효과를 사전에 모의 평가하고 있다. 그러나 이러한 수질모델을 적용하기 위해서는 복잡한 형식의 입력자료 구축단계가 요구되고 있으며 모델을 통한 타당한 분석결과를 산출하였음에도 불구하고 모델 자체의 표현의 한계성으로 인하여 효과적인 의사결정 자료로서의 활용이 미약한 실정이다. 본 연구는 GSIS를 이용한 하천수질모의에 관한 연구로서, 기존 수질모델의 이러한 제약을 극복하고자 GSIS환경에서 유역별 오염부하량을 산정하고 입력자료를 생성하며 모의결과를 시각화하는 인터페이스를 개발함에 있어 모델의 전ㆍ후처리과정에 GSIS를 적용하는 유연한 통합(Flexible coupling) 방법을 이용하여 수질모델과 GSIS를 통합하였다. 수질모델로는 기존의 하천수질모델 중 우리나라의 실정에 적합하여 비교적 정확하고 또한 그 적용이 간단하여 많은 지역에서 실제 적용되어 그 적용성이 검증된 QUAL2E 모델을 사용하였다.