• 한국수자원학회:학술대회논문집
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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.

• 상하수도학회지
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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.

• 상하수도학회지
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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.

• 상하수도학회지
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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

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회(2)
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• pp.1112-1113
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• 2005

• 한국산학기술학회논문지
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• 제12권11호
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• pp.5290-5299
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• 2011

본 연구의 궁극적인 목적은 물의 효율적 공급과 관리를 위한 한 방안을 제시하는데 있다. 그 총체적 접근의 일환으로, 전체 물 순환 중 도시용수를 대상으로 각 사용테마(주거, 상업, 공업 등)별 수요예측모형을 개발하고 개발된 모델을 적용한 GIS기반 정보시스템 구현방안을 제시하는데 본 논문의 목적이 있다. 이를 위해 적절한 연구대상지의 평가 및 선정, 테마별 센서의 설치위치 및 종류 선정, 센서를 포함한 무선통신인프라 및 현장서버의 설치가 이루어졌다. 그리고 통신프로토콜과 실시간 데이터 모니터링시스템이 개발되었다. 다음으로 도시용수 및 관련시설 데이터의 GIS DB화 과정이 수행되었으며, 용수시설 및 실시간 모니터링된 유량 데이터를 활용할 GIS기반 관리시스템이 설계되고 구현 청사진이 제시된다.

• 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.

• 대한공간정보학회지
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• 제19권3호
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• pp.83-90
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• 2011

본 논문의 목적은 공업용수 사용 추세패턴 모형을 개발하고 개발된 모형이 GIS시스템내에서 활용될 수 있는 청사진을 제시하는데 있다. 연구내용은, 사용데이터의 수집을 위해 실시간 모니터링 테크닉이 도입되었고 실시간 데이터는 5분단위로 센서 및 현장서버로부터 관리서버로 전송되었다. 취득된 데이터는 선택된 다항식에 대입되었고 결과로 요일별, 각 월의 일평균 수요모형들이 개발되었다. 도출된 모형들은 일련의 검증과정을 거쳐 최종 모형으로 압축선택되며 평균모형으로 변환되었다. 변환된 평균모형의 도식화를 통해 공업용수 수요패턴분석이 이루어졌다. 연구결과로, 수요패턴은 상당한 일관성을 보이고 있어 확률높은 요일별, 또는 계절별 수요예측이 가능하다는 결론이 도출되었다. 또한 이러한 예측모형을 활용할 정보화도구로서 GIS의 활용방안이 제시된다.

• 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.