• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.1910-1914
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• 2007

This paper employs a relatively new technique of Artificial Neural Network (ANN) to forecast water demand of Daegu city. The ANN model used in this study is a single hidden layer hierarchy model. About seventeen sets of historical water demand records and the values of their socioeconomic impact factors are used to train the model. Also other regression and time serious models are investigated for comparison purpose. The results present the ANN model can better perform the issue of urban water demand forecasting, and obtain the correlation coefficient of $R^2$ with a value of 0.987 and the relative difference less than 4.4% for this study.

• Journal of Korean Society of Water and Wastewater
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• 제28권4호
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• pp.377-389
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• 2014

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and this has led to various studies regarding energy saving and improvement of water supply reliability. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The concepts was demonstrated through application to observed from water plant (A) in the South Korea. Various statistics (e.g. the efficiency coefficient, the correlation coefficient, the root mean square error, and a maximum error rate) were evaluated to investigate model efficiency. The ensemble based model with an cross-validate prediction procedure showed better predictability for water demand forecasting at different temporal resolutions. In particular, the performance of the ensemble model on hourly water demand data showed promising results against other individual prediction schemes.

• Journal of Korean Society of Water and Wastewater
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• 제12권4호
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• pp.43-52
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• 1998

The purpose of this study is to establish a method of estimating the daily urban water demend using Backpropagation algorithm is part of ANN(Artificial Neural Network). This method will be used for the development of the efficient management and operations of the water supply facilities. The data used were the daily urban water demend, the population and weather conditions such as treperarture, precipitation, relative humidity, etc. Kwangju city was selected for the case study area. We adjusted the weights of ANN that are iterated the training data patterns. We normalized the non-stationary time series data [-1,+1] to fast converge, and choose the input patterns by statistical methods. We separated the training and checking patterns form input date patterns. The performance of ANN is compared with multiple-regression method. We discussed the representation ability the model building process and the applicability of ANN approach for the daily water demand. ANN provided the reasonable results for time series forecasting.

• Journal of Korean Society of Water and Wastewater
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• 제37권6호
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• pp.363-373
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• 2023

Numerous studies have established a correlation between sociodemographic characteristics and water usage, identifying population as a primary independent variable in mid- to long-term demand forecasting. Recent dramatic sociodemographic changes, including urban concentration-rural depopulation, low birth rates-aging population, and the rise in single-person households, are expected to impact water demand and supply patterns. This underscores the necessity for operational and managerial changes in existing water supply systems. While sociodemographic characteristics are regularly surveyed, the conducted surveys use aggregate units that do not align with the actual system. Consequently, many water demand forecasts have been conducted at the administrative district level without adequately considering the water supply system. This study presents an upward water demand forecasting model that accurately reflects real water facilities and consumers. The model comprises three key steps. Firstly, Statistics Korea's SGIS (Statistical Geological Information System) data was reorganized at the DMA level. Secondly, DMAs were classified using the SOM (Self-Organizing Map) algorithm to consider differences in water facilities and consumer characteristics. Lastly, water demand forecasting employed the PCR (Principal Component Regression) method to address multicollinearity and overfitting issues. The performance evaluation of this model was conducted for DMAs classified as rural areas due to the insufficient number of DMAs. The estimation results indicate that the correlation coefficients exceeded 0.9, and the MAPE remained within approximately 10% for the test dataset. This method is expected to be useful for reorganization plans, such as the expansion and contraction of existing facilities.

• 수도
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• 통권40호
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• pp.32-41
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• 1987

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2005년도 학술발표회(2)
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• pp.1112-1113
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• 2005

• Journal of the Korea Academia-Industrial cooperation Society
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• 제12권11호
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• pp.5290-5299
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• 2011

The ultimate purpose of this research is to propose a method to improve water supply management efficiency. As an effort to solve this comprehensive problem, the purposes of this paper are summarized into the following two main subjects. One is the development of a series of demand forecasting models targeting for each theme of urban water such as residential, commercial, industrial water. The other is the suggestion on the development and utilization plan of a GIS-based information system where the developed models are incorporated. For these, a series of efforts were performed such as evaluating and choosing of the candidate field areas, selecting a proper sensor and an installation point for each theme. Installed are sensors, a wireless communication infrastructure, and a field data acquisition and management server. Developed are a protocol for the wireless communication and a real-time data monitoring system. Nextly, the urban water facility-related and other necessary data were handled to make those into a series of GIS-ready databases. Finally, a GIS-based management system was designed and a blueprint for the implementation is suggested.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.147-154
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• 2024

The continuous increase in urban population due to migration of mases from rural areas to big cities has set urban water supply under serious stress. Urban water resources face scarcity of available water quantity, which ultimately effects the water supply. It is high time to address this challenging problem by taking appropriate measures for the improvement of water utility services linked with better understanding of demand side management (DSM), which leads to an effective state of water supply governance. We propose a dynamic framework for preventive DSM that results in optimization of water resource management. This paper uses Agent Based Modeling (ABM) with Digital Twin (DT) to model water consumption behavior of a population and consequently forecast water demand. DT creates a digital clone of the system using physical model, sensors, and data analytics to integrate multi-physical quantities. By doing so, the proposed model replicates the physical settings to perform the remote monitoring and controlling jobs on the digital format, whilst offering support in decision making to the relevant authorities.

• Journal of Korean Society for Geospatial Information Science
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• 제19권3호
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• pp.83-90
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• 2011

The purpose of this study is to develop a series of trend analysis models for industrial water consumption and to propose a blueprint for the integration of the developed models with GIS. For the consumption data acquisition, a real-time sensing technique was adopted. Data were transformed from the field equipments to the management server in every 5 minutes. The data acquired were substituted to a polynomial formula selected. As a result, a series of models were developed for the consumption of each day. A series of validation processes were applied to the developed models and the models were finalized. Then the finalized models were transformed to the average models representing a day's average consumption or an average daily consumption of each month. Demand pattern analyses were fulfilled through the visualization of the finally derived models. It has founded out that the demand patterns show great consistency and, therefore, it is concluded that high probability of demand forecasting for a day or for a season is available. Also proposed is the integration with GIS as an IT tool by which the developed forecasting models are utilized.

• Environmental Engineering Research
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• 제15권3호
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• pp.135-140
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• 2010

With the various urban characteristics of each city, the existing water demand prediction, which uses average liter per capita day, cannot be used to achieve an accurate prediction as it fails to consider several variables. Thus, this study considered social and industrial factors of 164 local cities, in addition to population and other directly influential factors, and used main substance and cluster analyses to develop a more efficient water demand prediction model that considers unique localities of each city. After clustering, a multiple regression model was developed that proved that the $R^2$ value of the inclusive multiple regression model was 0.59; whereas, those of Clusters A and B were 0.62 and 0.74, respectively. Thus, the multiple regression model was considered more reasonable and valid than the inclusive multiple regression model. In summary, the water demand prediction model using principal component and cluster analyses as the standards to classify localities has a better modification coefficient than that of the inclusive multiple regression model, which does not consider localities.