• Journal of Korean Society of Water and Wastewater
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• v.21 no.3
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• pp.305-314
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• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model

• 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.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.4
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.5
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• pp.19-28
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• 2016

This study was to design the optimal locations of the water level monitoring to quantify the agricultural water use in irrigation area supplied from an agricultural reservoir. In most of agricultural areas without TM/TC (Tele-Monitoring and Tele-Control) or monitoring network, irrigation water have been supplied on conventional experience and agricultural reservoir have been operated based on the operating simulation results by HOMWRS (Hydrological Operation Model for Water Resources System). Therefore, this study quantified the amount of agricultural water use in an irrigation area (Musu Reservoir, Jincheon-gun) by establishing water level monitoring network and analyzed the agricultural water saving effect. According to the evaluation of the economic values for water saving effect, the saving agricultural water of 1.7 million ton was analyzed to have economic values of 0.85 million won as water for living, and 1.78 million won as water for industrial use. It is identified to secure economic feasibility of the new water monitoring network by establishing one monitoring point in the entrance, irrigation area and endpoint through the economic analysis.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.1
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• pp.101-107
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• 1997

Recently, as the raw water quality becomes to be polluted and the seasonal and local variation of water quality becomes to be severe, an exact control of coagulant dosing have been required in the water treat- ment plant. The amounts of coagulant is related to the raw water quality such as turbidity, alkalinity, water temperature, pH and edectrical conductivity. However the process of chemical reaction has not been clarified so far, so the dosing rate has been decided by jar-test, which is taken one or two hours. For the sake of this coagulant dosing control, fuzzy neural network to fuse fuzzy logic and neural network was proposed, and the scheme was applied to automatic determination of coagulant dosing rate. This controller can automatically identify the if-then rules and tune the membership functions by utilizing expert's cintrol data. It is shown that determination of coagulant dosing rate according to real time sensing of water quality is very effect.

• Journal of Environmental Impact Assessment
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• v.14 no.2
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• pp.55-62
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• 2005

This study applied the Neural Network and Fuzzy theory to show water-purity control and preventive measure in water quality forecasting of the future river. This study picked out NAJU and HAMPYUNG as the subject of investigation and used monthly the water quality and the outflow data of KWANGJU2, NAJU, YOUNGSANNPO and HAMPYUNG from 1995 to 1999 to forecast BOD, COD, T-N, T-P water density. The datum from 1995 to 1999 are used for study and that of 2000 are used for verification. To develop model of water quality forecasting, firstly, this research formed Neural Network model and divided Neural Network model into two case - the case of considering lag and not considering. And this study selected optimal Neural Network model through changing the number of hidden layer based on input layer(n) from n to 3n. Through forecasting result, the case without considering lag showed more precise simulated result. Accordingly, this study intended to compare, analyse that Fuzzy model using the method without considering lag with Neural Network model. As a result, this study found that the model without considering lag in Neural Network Network shows the most excellent outcome. Thus this study examined a forecasting accuracy, analyzed result and verified propriety through appling the method of water quality forecasting using Neural Network and Fuzzy Algorithms to the actual case.

• Water Engineering Research
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• v.4 no.2
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• pp.59-68
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• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.323-327
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• 2006

During the past decades in South Korea, there have been several projects to reduce water demand and save water for paddy irrigation system by automation. This is called as intensive water management system by telemetering of paddy ponding depth and canal water level and telecontrol of water supply facilities. This study suggests a method of constructing topology-based irrigation network system using GIS tools. For the network modeling, a typical agricultural watershed included reservoirs, irrigation and drainage canals, pumping stations was selected. ArcHydro tools composed of edge, junction, waterbody and watershed were used to construct hydro-network. ArcHydro Model was then designed and the network was successfully built using the HydroID. Visualization using ArcHydro tools could display table property of each object. ArcHydro Model was linked to Agricultural Water Demamd and Supply Estimation System (AWDS) which developed by Korea Rural Community and Agriculture Corporation (KRC) to extract information of the study area. And menu of supply facilities information, demand analysis and supply analysis constructed for information acquisition and visualization of acquired informations.

• Korea Trade Review
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• v.47 no.3
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• pp.75-92
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• 2022

This study aims to analyze the International trade network of Water transport service using Social Network Analysis for defining the status of Korean Water transport industry. This study use World Input-Output Table of Asian Development Bank from 2000 to 2020 and build the International trade matrix of Water transport service from that. Therefore, this study analyze Out-degree centrality, In-degree centrality and betweenness centrality of Korea and other main countries in the matrix of World Water transport industry. As a result, Korea rank above 10th in the all centralities and the total output also rank 8th in the world, therefore, this study show the importance of Korean Water transport industry in the world. However, Singapore has the highest centrality in the world, even though China has the largest Total output among 63 countries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.