• Journal of Environmental Impact Assessment
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• v.21 no.4
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• pp.543-552
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• 2012

Namgang mid-watershed is located in downstream of Nakdong river basin. There are many pollution sources arround this area and it's control is important to manage a water quality of Nakdong river. A target year of Namgang mid-watershed water environment management plan is 2013. To predict a water quality at downstream of Namgang, we have investigated and forecasted the pollutant source and it's loading. There are some plan to construction the sewage treatment plants to improve the water quality of Nam river. Those are considered on predicting water quality. As results, it is shown that the population is 343,326 and sewerage supply rate is 79.2% and the livestock is 1,662,000 in Namgang mid-watershed. It is estimated that the population is 333,980, the sewerage supply rate is 86.9% in 2013. The milk cow and cattle were estimated upward and the pigs were downward by 2013. The generated loading of BOD and TP is 75,957 kg/day and 4,311 kg/day, discharged loading is 18,481 kg/day and 988 kg/day respectively in 2006. It were predicted upward the discharged loading of BOD and TP by 4.08% and 6.3% respectively. The results of water quality prediction of Namgang4 site were 2.5 mg/L of BOD and 0.120 mg/L of TP in 2013. It is over the target water quality at that site in 2015 about 25.0% and 9.1% respectively. Consequently, there need another counterplan to reduce the pollutants in that mid-watershed.

• Korean Journal of Environmental Agriculture
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• v.19 no.3
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• pp.252-258
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• 2000

SWMM and WASP5 were applied for pollutant loading estimate from watershed and reservoir water quality simulation, respectively, to predict estuarine reservoir water quality. Application of natural systems to improve estuarine reservoir water quality was reviewed, and its effect was predicted by WASP5. Study area was the Hwa-Ong reservoir in Hwasung-Gun, Kyonggi-Do. Procedures for estimation of pollutant loading from watershed and simulation of corresponding reservoir water quality were reviewed. In this study, SWMM was proved to be an appropriate watershed model to the nonurban area, and it could evaluate land use effects and many hydrological characteristics of catchment. WASP5 is a well known lake water quality model and its application to the estuarine reservoir was proved to be suitable. These models are both dynamic and the output of SWMM can be linked to the WASP5 with little effort, therefore, use of these models for reservoir water quality prediction in connection was appropriate. Further efforts to develop more logical and practical measures to predict reservoir water quality are necessary for proper management of estuarine reservoirs.

• Smart Media Journal
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• v.11 no.2
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• pp.39-52
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• 2022

In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

• Journal of Korean Port Research
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• v.12 no.2
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• pp.313-322
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• 1998

The distributions of chemical oxygen demand (COD) and suspended solid (SS) in Ulsan Bay were simulated and reproduced by a numerical ecosystem model for the practical application to the management of marine water quality and the prediction of water quality change due to coastal developments or the constructions of breakwater and marine facilities. Comparing the computed with the observed data of COD and SS in Ulsan bay the results of simulation were found to be good enough to satisfy the practical applications.

• Journal of Korean Society on Water Environment
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• v.20 no.3
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• pp.223-230
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• 2004

The QUAL2E and Box-Jenkins time series model were applied to the Miho river, a main tributary of the Geum river, to predict water quality. The models are widely used to predict water quality in rivers and watersheds because of its accuracy. As results of the study, we concluded as follows: Pollutant loadings in upper stream of Miho river were determined to 57,811 kgBOD/d, 19,350 kgTN/d, and 5,013 kgTP/d. The loading of TN in Mushim river was 19,450 kgTN/d, respectively. As the mass loadings were compared with pollutant sources, it concluded that the farming livestock contributed highly to mass emissions of BOD and TP and the population contributed to TN mass loading. The observed water quality values were applied to the models to verify and the models were used to predict the water quality. The QUAL2E Model predicted the concentrations of DO, BOD, TN and TP with high accuracy, but not for E-Coli. The Box-Jenkins time series model also showed high prediction for DO, BOD and TN. However, the concentrations of TP and E-Coli were poorly predicted. The result shows that the QUAL2E model is more applicable in Miho basin for prediction of water quality compared to Box-Jenkins time series model.

• Journal of Ocean Engineering and Technology
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• v.22 no.2
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• pp.34-41
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• 2008

As a basic study for developing a forecasting/estimating system that predicts water quality changes when Deep Sea Water (DSW) drains to the ocean after using it, this study was carried out as follows: 1) numerical simulation of the present state at DSW developing region in the East sea using SWEM, 2) numerical prediction of water quality changes by effluent DSW, 3) analysis of influence degree 'With defined DEI (DSW effect index) at F station. On the whole, when DSW drained to the ocean, Chl-a, COD and water-temperature were decreased and DIN, DIP and DO were increased by effluent DSW, and Salinity was steady. According to analysis of influence degree, the influence degree of DIN was the highest and it was high in order of Chl-a, COD, Water-temperature, DO, DIP and Salinity. The influence degree classified by DSW effluent position was predicted that suiface outflow was lower than bottom outflow. Ad When DSW discharge increased 10 times, the influence degree increased about $5{\sim}14$ times.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.48-55
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• 2024

The safety-related raw water system's strong operational condition supports the radiation defense and biological shield of nuclear plant containment structures. Gaps and failures in maintaining proper working condition of main equipment like pump were among the most common causes of unavailability of safety related raw water systems. We integrated the advanced data analytics tools to evaluate the maintenance records of water systems and gave special consideration to deficiencies related to pump. We utilized maintenance data over a three-and-a-half-year period to produce metrics like MTBF, MTTF, MTTR, and failure rate. The visual analytic platform using tableau identified the efficacy of maintenance & deficiency in the safety raw water systems. When the number of water quality violation was compared to the other O&M deficiencies, it was discovered that water quality violations account for roughly 15% of the system's deficiencies. The pumps were substantial contributors to the deficit. Pump availability was predicted and optimized with real time data using response surface method. The prediction model was significant with r-squared value of 0.98. This prediction model can be used to predict forth coming pump failures in nuclear plant.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.3
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• pp.118-124
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• 1998

To use chlorine residual as an surrogate parameter of the water quality change during the transportation in the water distribution system(WDS), the correct prediction model of chlorine residual must be established in advance. This paper shows the procedure and the result of applying the water quality model to the field WDS. To begin with, hydraulic model was calibrated and verified using fluoride as an tracer. And chlorine residual was predicted through simulation of water quality model. This predicted value was compared with the observed value. With adjusting the bulk decay coefficient(kb) and the wall decay coefficient(kw) according to the pipewall environment, the predicted chlorine residual can represent the observed value relatively well.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.9-16
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• 2009

According to the construction of infrastructure for the water resource management services, the importance of the eco-friendly reservoir tank management is being spotlighted. In this paper, we proposed the eco-friendly reservoir tank management using prediction for improving the water quality and on-line managing efforts of reservoir tanks. The proposed method defined the context and environment of the reservoir tank and predicted the profited service according to the pump motion, the solar battery, the chemicals, the water level, the telephone line, and the modem using collaborative filtering. To evaluate the performance of the eco-friendly reservoir tank management system using prediction, we conducted sample T-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction. Accordingly, the satisfaction and the quality of services will be improved the efficient prediction by supporting the context information as well as the environment information.