• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1576-1589
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• 2006

Most of surface water ways in Egypt suffer from the infestation of aquatic weeds especially submerged ones which cause lots of problems for the open channels and the water structures such as increasing water losses, obstructing the water flow, and reducing the efficiency of the water structures. Accurate simulation of the water flow behavior in such channels is very essential for water distribution decision makers. Artificial Neural Network (ANN) has been widely utilized in the past ten years in civil engineering applications for the simulation and prediction of the different physical phenomena and has proven its capabilities in the different fields. The present study aims towards introducing the use of ANN technique to model and predict the impact of the existence of submerged aquatic weeds on the hydraulic performance of open channels. Specifically the current paper investigates utilizing the ANN technique in developing a simulation and prediction model for the flow behavior in an open channel experiment that simulates the existence of submerged weeds as branched flexible elements. This experiment was considered as an example for implementing the same methodology and technique in a real open channel system. The results of current manuscript showed that ANN technique was very successful in simulating the flow behavior of the pre-mentioned open channel experiment with the existence of the submerged weeds. In addition, the developed ANN models were capable of predicting the open channel flow behavior in all the submerged weeds' cases that were considered in the ANN development process.

• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.2
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• pp.23-32
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• 1993

This paper describes the background and the development of a hydrologic network flow model. The model was development to simulate daily water demand and supply for selected stream reaches within a watershed, and used as a tool for evaluating, simulating, and planning a water resources system. The proposed network flow model considers daily runoff from subareas, various water demands, and diversion structures within each subarea. Daily streamflow at a reach is simulated after balancing the water demands from subareas. The lateral inflow from subareas is simulated using a modified tank model. Total water demands consist of the daily demands for agricultural, domestic, industrial, livestock, fishery, and environmental uses within a rural district. The return flow, diversions from sources and storage components such as reservoirs were also incorporated into the mode l . The developed model is a generalized version that may be applied to different combinations of river reaches for a given system. This may help potential users identify areas where water supply does not suffice the demands for different time horizons.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.5
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• pp.421-430
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• 2017

In this study, a model was developed to predict for Disinfection By-Products (DBPs) generated in water supply networks and consumer premises, before and after the introduction of advanced water purification facilities. Based on two-way ANOVA, which was carried out to statistically verify the water quality difference in the water supply network according to introduce the advanced water treatment process. The water quality before and after advanced water purification was shown to have a statistically significant difference. A multiple regression model was developed to predict the concentration of DBPs in consumer premises before and after the introduction of advanced water purification facilities. The prediction model developed for the concentration of DBPs accurately simulated the actual measurements, as its coefficients of correlation with the actual measurements were all 0.88 or higher. In addition, the prediction for the period not used in the model development to verify the developed model also showed coefficients of correlation with the actual measurements of 0.96 or higher. As the prediction model developed in this study has an advantage in that the variables that compose the model are relatively simple when compared with those of models developed in previous studies, it is considered highly usable for further study and field application. The methodology proposed in this study and the study findings can be used to meet the level of consumer requirement related to DBPs and to analyze and set the service level when establishing a master plan for development of water supply, and a water supply facility asset management plan.

• Journal of Korean Society of Water and Wastewater
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• v.13 no.2
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• pp.23-33
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• 1999

This study is to investigate the effect of C factor errors on the analysis of water distribution systems. For this purpose, an artificial distribution network and a real distribution network were selected as the study networks. Results are as follows. 1. The C factor of a pipe which has small velocity didn't give significant effect on the analysis of a water distribution system. 2. The effect of decreased value of C factors give more influence on the analysis of water distribution systems than that of the increased values. 3. For the C factor calibration, errors of the residual water heads as well as those of the head losses should be considered together. 4. In the analysis of water distribution systems, changes of C factors can give influences only on the nodes which locate behind the pipe. Therefore, this characteristics should be considered in the selection of nodes for the measurement of water heads.

• 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.

• Water for future
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• v.25 no.2
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• pp.12-18
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• 1992

• Water for future
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• v.38 no.5 s.148
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• pp.73-80
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• 2005

• Journal of Environmental Impact Assessment
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• v.19 no.3
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• pp.307-314
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• 2010

Brebbia's model has been analyzed to develop the appropriate waterworks management system in Korea, and compared with the conventional models such as EPANET, WaterCad, and InfoWorks. The hydraulic theory of the models was analyzed. Each model's numerical techniques, required parameters, input data and operational methodologies, restrictions, practical applicability and other aspects were investigated. In order to check the validity of Brebbia model, the comparative analysis with EPANET, WaterCAD, and InfoWorks models was performed for linear and nonlinear cases. To find out advantages and disadvantages of each model, the modeling was performed for a simple network and for more complicated A city waterworks system, and the three models applicability was examined. Finally, optimal modeling technique and a model suitable for the use in Korea was suggested, and the problems related to present projects of waterworks management system in Korea were analyzed.

• Water for future
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• v.27 no.4
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• pp.161-171
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• 1994

The water distribution system problem consists of finding a minimum cost system design subject to hydraulic and operation constraints. The design of new branching network in a paddy irrigation system is presented here. The program based on the linear programming formulation is aimed at finding the optimal economical combination of two main factors: the capital cost of pipe network and the energy cost. Two loading conditions and booster pumps for design of pipe network are considered to obtain the least cost design.

• Journal of Environmental Science International
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• v.11 no.3
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• pp.201-207
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• 2002

This study was carried out to evaluate the artificial neural network algorithm for water quality forecasting in Chungju lake, north Chungcheong province. Multi-layer perceptron(MLP) was used to train artificial neural networks. MLP was composed of one input layer, two hidden layers and one output layer. Transfer functions of the hidden layer were sigmoid and linear function. The number of node in the hidden layer was decided by trial and error method. It showed that appropriate node number in the hidden layer is 10 for pH training, 15 for DO and BOD, respectively. Reliability index was used to verify for the forecasting power. Considering some outlying data, artificial neural network fitted well between actual water quality data and computed data by artificial neural networks.