• Water for future
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• v.29 no.1
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• pp.141-150
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• 1996

Steady state analysis of pressure and flow in water supply piping systems is a problem of great importance in hydraulic engineering. The basic equations consist of continuity equation and energy equation. The network equations are solved iteratively by using linear solution method. The resulting linear simultaneous equations are solved by frontal method. Frontal method, which is suitable to sparse matrix, gathers only non-zero entries in coefficient matrix. The suggested methodology can analyze faster than the existing routines by using smaller computer memory. The model presented in this study shows accurate and efficient results for various piping systems.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.556-570
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• 2021

This study presents the data analysis results of groundwater chemistry and the occurrence of fluoride in groundwater obtained from the groundwater quality monitoring network of Korea. The groundwater data were collected from the National Groundwater Information Center and censored for erratic values and charge balance (±10%). From the geochemical graphs and various ionic ratios, it was observed that the Ca-HCO₃ type was predominant in Korean groundwater. In addition, water-rock interaction was identified as a key chemical process controlling groundwater chemistry, while precipitation and evaporation were found to be less important. According to a non-parametric trend test, at p=0.05, the concentration of fluoride in groundwater did not increase significantly and only 4.3% of the total groundwater exceeded the Korean drinking water standard of 1.5 mg/L. However, student t-tests revealed that the fluoride concentrations were closely associated with the lithologies of tuff, granite porphyry, and metamorphic rocks showing distinctively high levels. This study enhances our understanding of groundwater chemical composition and major controlling factors of fluoride occurrence and distribution in Korean groundwater.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.425-425
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• 2023

상수도시스템의 지속가능한 운영을 위해서는 에너지를 효율적으로 활용하는 것이 필수적이다. 따라서, 본 연구에서는 상수도시스템에서 고압구간의 압력조절을 통해 에너지 측면의 최적 운영기법을 제안하였다. 제안된 최적 운영기법은 마이크로 터빈을 이용하여 에너지 회수를 통한 압력저하와 이로 인한 누수량 저감을 동시에 고려하였다. 본 연구에서 사용한 최적화기법으로는 Multi-objective harmony search 알고리즘을 사용하였으며, 상수도 시스템에서 마이크로 터빈을 모의하기 위해서 EPANET의 압력조절 밸브 (Pressure reduction valve)를 활용하여 압력강하에 따른 에너지 회수를 고려하였다. 제안된 최적운영 기법을 검증하기 위해 Benchmark networks (e.g., 5-nodes network, 25-nodes network)을 적용하였으며, 본 연구를 통해 마이크로 터빈의 최적 규모와 설치위치, 개수를 결정할 수 있으며, 이에 따른 에너지 회수와 누수감소 측면에서 상수도시스템의 지속가능한 운영에 효과적으로 이용될 수 있을 거라 기대된다.

• KCID journal
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• v.17 no.2
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• pp.95-104
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• 2010

Water quality data for 10 years (2000~2009) from about 826 reservoirs that are operated as a agricultural water quality survey network were analyzed in order to seek water quality management plan based on physical and chemical characteristics of agricultural reservoirs. The 95% reservoirs that exceed agricultural water quality standard of Chl-a (35mg/ $m^3$) had effective water depth shallower than 5m. The reason was that the reservoirs had more inflows of nutrient salts from the watershed, bigger surface water area of weak structure to algae occurrence. As the reservoirs of effective water depth shallower than 5m cover 49% of benefited area for irrigation, it is critical for agricultural water quality management of the reservoirs. The water quality of reservoir with shallower than 5m effective water depth was worse than reservoir with deeper than 5m effective water depth. Therefore, it is desirable that effective water depth of reservoirs make more than 5m for water quality management by building the bank higher and dredging the bottom of reservoirs.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.602-609
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• 2011

In this paper, we standardized the water operation system picture, process control logic, realtime database and system configuration. All aspects, including monitoring & controlling processes, symbols such as pumps, valves and pipes were standardized. As a result we have developed a specialized Integrated water operation system, iWater. We have developed a variety of advanced application programs that are essential for water treatment systems, such as IWS (Integrated Warning system), MBO(modbus opc)/LSE(LS ethernet) driver, video monitoring, self diagnosis system, network monitoring, etc. IWS prevents water supply accidents by using a variety of alarms and warning messages. Drivers have the flexibility to communicate with other 3rd party systems. We expect that iWater will eliminate any concerns regarding water-related issues while also promoting the production and fair distribution of clean water.

• The Journal of Bigdata
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• v.1 no.1
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• pp.9-17
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• 2016

This study identifies major features in water supply and introduces important factors in water services based on the information from data mining analysis of water quantity and water pressure measured from sensors. It also suggests more accurate methods using multiple regression analysis and neural network in predicting short term prediction of water demand in water service. A small block of a county is selected for the data collection and tests. There isa water demand on business such as public offices and hospitalstoo in this area. Real stream data from sensors in this area is collected. Among 2,728 data sets collected, 2,632 sets are used for modelling and 96 sets are used for testing. The shows that neural network is better than multiple regression analysis in their prediction performance.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.183-192
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• 2012

Although underground water resource has relatively less pollution rate compared with surface water, its recovery faces many difficulties due to poor management. Our country monitors underground water to manage it effectively through auxiliary observation network for underground water. In this paper, we suggest water-well auto measure system based on Geosensor for business efficiency increase of water-well management and realtime monitering. In this system is consist of user GUI(Graphic User Interface) composed with water-well information and movement sub-observation network prototype system composed with GPS(Global Positioning System) and wireless sensor node such as water temperature, water level, electrical conductivity. In this system is using the light of the sun for self-power, variety water-well information collected wireless sensor node was a wireless transmitting/receiving a using CDMA(Code Division Multiple Access) module. Also, for promote with user ease, user GUI express that water-well collected in GIS(Geographic Information System) map. For performance evaluation of the proposed system, we perform experiment using sensing information through designed sub-observation network. And we was proved superiority of the proposed system through qualitative evaluation with other paper.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.505-515
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• 2000

THe purpose of this study is to forecast of runoff hydrographs according to rainfall event in a stream. The neural network theory as a hydrologic blackbox model is used to solve hydrological problems. The Back-Propagation(BP) algorithm by the Levenberg-Marquardt(LM) techniques and Radial Basis Function(RBF) network in Neural Network(NN) models are used. Runoff hydrograph is forecasted in Bocheongstream basin which is a IHP the representative basin. The possibility of a simulation for runoff hydrographs about unlearned stations is considered. The results show that NN models are performed to effective learning for rainfall-runoff process of hydrologic system which involves a complexity and nonliner relationships. The RBF networks consist of 2 learning steps. The first step is an unsupervised learning in hidden layer and the next step is a supervised learning in output layer. Therefore, the RBF networks could provide rather time saved in the learning step than the BP algorithm. The peak discharge both BP algorithm and RBF network model in the estimation of an unlearned are a is trended to observed values.