• 수도
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• s.30
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• pp.24-48
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• 1984

• Journal of Korean Society of Water and Wastewater
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• v.12 no.4
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1112-1113
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• 2005

• Proceedings of the Korea Water Resources Association Conference
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• 1998.05a
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• pp.179-184
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• 1998

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1488-1494
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• 2011

In this paper, we propose an algorithm using linear regression model that forecasts the demand of heated water in winter. To supply heated water to apartments, stores and office buildings, Korea District Heating Corp.(KDHC) operates boilers including electric power generators. In order to operate facilities generating heated water economically, it is essential to forecast daily demand of heated water with accuracy. Analysis of history data of Kangnam Branch of KDHC in 2006 and 2007 reveals that heated water supply on previous day as well as temperature are the most important factors to forecast the daily demand of heated water. When calculated by the proposed regression model, mean absolute percentage error for the demand of heated water in winter of the year 2006 through 2009 does not exceed 3.87%.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.965-977
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.713-719
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• 2009

In this paper, we develope a water demand forecasting algorithm using AR(Auto-regressive) and MLP(Multi-layer perceptron). To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "A" purification plant at Jeon-Buk province during 2007-2008, and then performed the proposed method with various input factors selected through various analyses. As noted in experimental results, the performance of three types model such as multi-regressive, AR(Auto-regressive), and AR+MLP(Auto-regressive + Multi-layer perceptron) show 5.1%, 3.8%, and 3.6% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict short-term water demand for the efficient operation of a water purification plant.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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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.