• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1995.12a
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• pp.9-21
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• 1995

A planning support system enhances our ability to use water capacity expansion as an urban growth management strategy. This paper reports the development of capacity expansion modeling of water supply as part of the continuing development of such a planning support system (PEGASUS: Planning Environment for Generation and Analysis of Spatial Urban Systems) to incorporate water supply, This system is designed from the understanding that land use and development drive the demand for infrastructure and infrastructure can have a significant influence on the ways in which land is developed and used. Capacity expansion Problems of water supply can be solved in two ways: 1) optimal control theory, and 2) mixed integer nonlinear programming (MINLP). Each method has its strengths and weaknesses. In this study the MINLP approach is used because of its strength of determining expansion sizing and timing simultaneously. A dynamic network optimization model and a water-distribution network analysis model can address the dynamic interdependence between water planning and land use planning. While the water-distribution network analysis model evaluates the performance of generated networks over time, the dynamic optimization model chooses alternatives to meet expanding water needs. In addition, the user and capacity expansion modeling-to-generate-alternatives (MGA) can generate alternatives. A cost benefit analysis module using a normalization technique helps in choosing the most economical among those alternatives. GIS provide a tool for estimating the volume of demanded water and showing results of the capacity expansion model.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.4
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• pp.581-589
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• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• Journal of Soil and Groundwater Environment
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• v.27 no.2
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• pp.24-35
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• 2022

This study was conducted to estimate groundwater demand and supply for agricultural activities in a frequent-drought area that requires implementation of optimal distribution plan utilizing the well-network system (WNS). The WNS has been considered as a viable strategic way of supplying groundwater to farmlands by connecting groundwater wells physically or virtually. The study area heavily relied on groundwater resources for irrigation up to 53% due to a lack of surface water resources. Two kinds of methods, HOMWRS software and FAO approach, were used for estimating irrigation water requirements for paddy and upland fields, respectively. During the latest 10 years (2010~2019), the water requirements was estimated to be 1,106 m³/day. The requirements notably increased to 1,121~4,004 m³/day during active farming season (May to September), which exceeded the total yield capacity of pre-existing groundwater wells (2,356 m³/day) in the area. Detailed and definite determination for groundwater demand and supply helped to determine optimal scale parameters of WNS. The WNS has achieved more balanced distribution of groundwater resources for irrigation over the study area.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.131-138
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• 2010

Due to the increased water demand and severe drought as an effect of the global warming, the effluent from wastewater treatment plants becomes considered as an alternative water source to supply agricultural, industrial, and public (gardening) water demand. The effluent from the wastewater treatment plant is a sustainable water source because of its good quality and stable amount of water discharge. In this study, the water reuse system was developed to minimize total construction cost to cope with the uncertain water demand in future using two-stage stochastic linear programming with binary variables. The pipes in the water reuse network were constructed in two stages of which in the first stage, the water demands of users are assumed to be known, while the water demands in the second stage have uncertainty in the predicted value. However, the water reuse system has to be designed now when the future water demands are not known precisely. Therefore, the construction of a pipe parallel with the existing one was allowed to meet the increased water demands in the second stage. As a result, the trade-off of construction costs between a pipe with large diameter and two pipes having small diameters was evaluated and the optimal solution was found. Three scenarios for the future water demand were selected and a hypothetical water reuse network considering the uncertainties was optimized. The results provide the information about the economies of scale in the water reuse network and the long range water supply plan.

• Journal of Digital Convergence
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• v.7 no.3
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• pp.103-111
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• 2009

In this paper, we introduce automatic meter reading system for tap water. The system is composed of automatic meter, router using RF and CDMA network, and data server. This system will easily extend to fire detect, gas, and electric charge meter system. In addition, this system will be used to monitoring a water leak and human which live in solitude. Proposed system is installed at Gimhae-City and Gijang-gun. As a result of the automatic meter reading system for tap water, The leak of water and complain of user is decreased. But The building cost is still an obstacle to expand into entire city.

• 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.

• IE interfaces
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• v.19 no.2
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• pp.106-116
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• 2006

In this study, the priority of risk factors in supplying water through water supply pipeline network was evaluated by PROMETHEE and ANP multi-criteria decision analysis. We chose 'corrosion', 'burst' and 'water pollution' in pipe as major reference criteria and selected eight risk factors to evaluate the priority, and then we compared the results of PROMETHEE with those of ANP. We also analyzed the results of the sensitivity analysis by changing the weights and parameters of preference functions in PROMETHEE. We investigated the possibility of integrating two methods by using the results of ANP as the weights of preference function in PROMETHEE. The priority of risk factors for supplying municipal water which is evaluated by this study may provide basic data to establish a contingency plan for accidents, or to establish the specific emergency response procedures.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.5
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• pp.335-349
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• 2021

As the frequency of seismic disasters in Korea has increased rapidly since 2016, interest in systematic maintenance and crisis response technologies for structures has been increasing. A data-based leading management system of Lifeline facilities is important for rapid disaster response. In particular, the water supply network, one of the major Lifeline facilities, must be operated by a systematic maintenance and emergency response system for stable water supply. As one of the methods for this, the importance of the structural health monitoring(SHM) technology has emerged as the recent continuous development of sensor and signal processing technology. Among the various types of SHM, because all machines generate vibration, research and application on the efficiency of a vibration-based SHM are expanding. This paper reviews a vibration-based pipeline SHM system for seismic disaster response of water supply pipelines including types of vibration sensors, the current status of vibration signal processing technology and domestic major research on structural pipeline health monitoring, additionally with application plan for existing pipeline operation system.

• Journal of Korea Water Resources Association
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• v.52 no.4
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• pp.291-300
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• 2019

If pipes are damaged in a water distribution network (WDN), adjacent valves are closed to isolate the pipes for repair. Due to the closed valves, parts of WDN are isolated from water supply sources. The isolated area is divided into Intended Isolation Area (IIA) and Unintended Isolation Area (UIA). The IIA occurs by intention to isolate the damaged pipe, while UIA is unintentionally disconnected from the sources due to IIA. Thus, the extension of isolated area and suspended flows are mainly affected by number and location of installed valves in WDN. In this study, optimization models were developed to determine optimal valve locations in WDN. In a single-objective model, total water supply suspension is minimized, while a multi-objective model intends to simultaneously minimize the suspended flow and valve installation cost. Optimal valve placement results obtained from both models were compared and analyzed using a sample application network.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.821-832
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• 1998

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. Fuzzy neuron, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an adaptive learning method by which a membership function and fuzzy rules were adapted for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water supplied to the city of Kwangju. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supplied (b) the mean temperature, and (c)the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.35% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.