• Journal of Korean Society of Water and Wastewater
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• v.14 no.2
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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.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.2
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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.

• Journal of Korean Society on Water Environment
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• v.20 no.4
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.47-49
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• 2005

• Korean Journal of Ecology and Environment
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• v.46 no.1
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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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• v.10
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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.

• Journal of Korean Society on Water Environment
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• v.30 no.1
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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.

• Journal of Korea Water Resources Association
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• v.38 no.7 s.156
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• pp.565-574
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• 2005

It is used water quality data that was measured at Pyeongchanggang real time monitoring stations in Namhan river. These characteristics were analyzed with the water qualify of rainy and nonrainy periods. TOC (Total Organic Carbon) data of rainy periods has correlation with discharge and shows high values of mean, maximum, and standard deviation. DO (Dissolved Oxygen) value of rainy periods is lower than those of 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 qualify forecasting models were applied. LMNN, MDNN, and ANFIS models have achieved the highest overall accuracy of TOC data. LMNN (Levenberg-Marquardt Neural Network) and MDNN (MoDular Neural Network) model which are applied for DO forecasting shows better results than ANFIS (Adaptive Neuro-Fuzzy Inference System). MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. The observation of discharge and water quality are effective at same point as well as same time for real time management. But there are some of real time water quality monitoring stations far from the T/M water stage. Pyeongchanggang station is one of them. So discharge on Pyeongchanggang station was calculated by developed runoff neural network model, and the water quality forecasting model is linked to the runoff forecasting model. That linked model shows the improvement of waterquality forecasting.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.2
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.3
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• pp.253-261
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• 2001

Nowadays, various water quality prediction models have been studied, then these models can support the method which evaluate the effect of various alternative water quality management by simulation without experimentation. But, It is necessary to create complex input data file for applying these water quality model and even though the appropriate result is extracted, it is impossible to use as decision making data effectively because of the limitation of expression of model itself. As this study is about the stream water quality modeling, for overcoming the model's above limitation, by developing an interface which can calculate the pollutant load of watershed, I could create a input data file and visualize the simulation result so that I was going to integrate water quality model and GSIS using Flexible coupling applied to GSIS in the pre-process and post-process on model. The QUAL2E model, used in this study, is verified by stream water quality model in previous various results of study and has many examples through previous study, because that is appropriate to water quality model, especially in Korea, and comparatively accurate and their usage is quite simple.