• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.367-367
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• 2012

기후변화로 인해 기온, 강수량, 습도 등의 기후를 예측하고 변화하는 환경에 적응해가며 생활하고 있다. 또한 여러 가지 외부적인 요인들의 영향을 받아 상수도 시설에서의 에너지 사용량도 영향을 많이 받는다. 하지만 이러한 상수도 시설의 사용량 변화로 인해 상수도 수요량의 변화량을 예측하는데 있어서 국내 연구 및 방법이 많이 부족한 상황이다. 이에 본 연구에서는 다중스케일을 기반으로 하는 비선형 예측 모형을 개발하고자 한다. 다중스케일 분석에서도 가장 우수한 분해 능력을 가지는 Wavelet Transform을 적용하여 시계열을 분해한 후 패턴인식 기반의 비선형 예측모형인 Support Vector Machine(SVM)을 적용하였다. 상수도 수요량의 예측 과정은 다음과 같다. 첫째, 상수도 수요량 자료를 Wavelet Transform 기법을 통하여 단순화 시킨다. 둘째, Global Wavelet Spectrum을 통하여 통계적으로 의미 있는 성분만을 추출하고 이를 해석 대상으로 한다. 셋째, 특정 주기를 갖는 유의한 독립성분들에 대해서 최적 지체시간을 결정한 후 SVM모형을 통해 예측 모형을 구축한다. 넷째, 나머지 성분에 대해서도 SVM 모형을 적용하여 예측을 실시한 후 앞서 예측된 성분과 모두 결합하여 최종적으로 예측시계열을 구성한다.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.5
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• pp.447-457
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• 2017

This study derived the unit of industrial water usage reflecting the latest industry trends. Available for establishing plans such as the master plan for water supply system and analyzed changes in the basic unit by a comparison with the current basic unit values. This study analyzed 4,038 samples with a sampling error of less than 1.5 % at the 95 % confidence level after removing outliers according to a log-normal distribution. As a result, the unit of industrial water usage per site area in the whole manufacturing industry was 7.11 m3/1,000m2/d. The ten industrial categories (C10, C13, C20, C21, C22, C25, C27, C30, C32, C33) showed a similar unit value compared to before, and the four industrials categories (C11, C17, C22, C31) showed a more unit value than before. With regard to the nine industrial categories (C14, C15, C16, C18, C19, C24, C26, C28, C29), the unit value decreased. Cases that companies examined before were the same as the companies examined in this study were analyzed. The result that the changes in the unit industrial water usage were reasonable was obtained. However, in some industrial categories (C17, C14, C24, C29), the unit value was changed by a small number of companies with large-scale water use or unit value of sampling had a large deviation. It was considered necessary to survey them periodically. The unit of industrial water usage derived by the survey in this study reflects the current industrial trends in 2016. Water use in manufacturing companies has continuously changed by the development of manufacturing technologies and simplification of manufacturing processes. In order to deal with this, it is considered necessary to survey the usage of industrial water periodically from a long-term perspective.

• Journal of Korean Society of Water and Wastewater
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• v.36 no.3
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• pp.149-165
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• 2022

The water consumption data of individual consumers must be analyzed and forecast to establish an effective water demand management plan. A k-mean cluster model that can monitor water use characteristics based on hourly water consumption data measured using automated meter reading devices and demographic factors is developed in this study. In addition, the quantification model that can estimate the daily water consumption is developed. K-mean cluster analysis based on the four clusters shows that the average silhouette coefficient is 0.63, also the silhouette coefficients of each cluster exceed 0.60, thereby verifying the high reliability of the cluster analysis. Furthermore, the clusters are clearly classified based on water usage and water usage patterns. The correlation coefficients of four quantification models for estimating water consumption exceed 0.74, confirming that the models can accurately simulate the investigated demographic data. The statistical significance of the models is considered reasonable, hence, they are applicable to the actual field. Because the use of automated smart water meters has become increasingly popular in recent year, water consumption has been metered remotely in many areas. The proposed methodology and the results obtained in this study are expected to facilitate improvements in the usability of smart water meters in the future.

• Journal of Korean Society of Water and Wastewater
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• v.32 no.5
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• pp.421-434
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• 2018

Water supply/intake pumps operation use 70~80% of power costs in water treatment plants. In the water treatment plant, seasonal and hourly differential electricity rates are applied, so proper pump scheduling can yield power cost savings. Accordingly, the purpose of this study was to develop an optimal water supply pump scheduling scheme. An optimal operation method of water supply pumps by using genetic algorithm was developed. Also, a method to minimize power cost for water supply pump operation based on pump performance derived from the thermodynamic pump efficiency measurement method was proposed. Water level constraints to provide sufficient disinfection performance in a clearwell and reservoirs were calibrated. In addition, continuous operation time constraints were calibrated to prevent frequent pump switching. As a result of optimization, savings ratios during 7 days in winter and summer were 4.5% and 5.1%, respectively. In this study, the method for optimal water pump operation was developed to secure disinfection performance in the clearwell and to save power cost. It is expected that it will be used as a more advanced optimal water pump operation method through further studies such as water demand forecasting and efficiency according to pump combination.

• Journal of the Korean Society for Marine Environment & Energy
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• v.1 no.2
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• pp.96-108
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• 1998

The management of water quality and fishery resources with a major environmental problem in eutrophic coastal sea is studied. The numerical experiments using the water-sediment quality model (WSQM) were carried out for the management of water quality at the Seto Inland Sea in Japan. The results of long-term water quality simulation showed responses of seawater quality to input loads to vary in different localities. A formula roughly forecasting water qualify to estimate the effect of loading abatement was proposed. The simulation for the improvement of seawater quality showed the abatements of nutrient loads such as total phosphorus (TP) and total nitrogen (TN) as well as organic loads such as chemical oxygen demand (COD) to be peformed in the eastern Seto Inland Sea from Bisan Seto to Osaka Bay. On the other hand, it is indicated that the increase of loading leads to the increase of primary production. while not straightly to the increase of fish production for the catch of fisheries.

• Korean Journal of Construction Engineering and Management
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• v.6 no.2 s.24
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• pp.61-68
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• 2005

The analysis described in this paper indicate the existence of a correlationship for housing demand and water supply ratio. Using subjective statistical data for the trend of population on regional area, water supply ratio and the number of households, the paper examines the correlationship of forecasting factors for apartments in the ways in which the tendency of demands for apartments and water supply ratio have been analyzed within small and mediumsized city. Differences in the correlationship on the several scale of a city are also taken into account in the analysis. The summary table of the tendency for housing supplies, population and water supply ratio on each scale of a city was generated using data from LAIB. This study attempted to address certain factors that are measurable within a specified paradigm, in order to investigate the extent to which the expectation of apartment supplies can be estimated from the correlationship of water supply ratio. Therefore, it can be suggested that the limited scale of a city are set to maintain the correlationship for housing demands and water supply ratio.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.322-323
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• 2022

The most necessary thing for the optimal operation of a water purification plant is to accurately predict the pattern and amount of tap water used by consumers. The required amount of tap water should be delivered to the drain using a pump and stored, and the required flow rate should be supplied in a timely manner using the minimum amount of electrical energy. The short-term demand forecasting required from the point of view of energy optimization operation among water purification plant volume predictions has been made in consideration of seasons, major periods, and regional characteristics using time series analysis, regression analysis, and neural network algorithms. In this paper, we analyzed energy management methods through AI-based complex sensor applicability analysis such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), which are types of cyclic neural networks.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.155-161
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• 2023

97.5% of our country's exports and 87.2% of imports are transported by sea, making ports an important component of the Korean economy. To efficiently operate these ports, it is necessary to improve the short-term prediction of port water volume through scientific research methods. Previous research has mainly focused on long-term prediction for large-scale infrastructure investment and has largely concentrated on container port water volume. In this study, short-term predictions for petroleum and liquefied gas cargo water volume were performed for Ulsan Port, one of the representative petroleum ports in Korea, and the prediction performance was confirmed using the deep learning model LSTM (Long Short Term Memory). The results of this study are expected to provide evidence for improving the efficiency of port operations by increasing the accuracy of demand predictions for petroleum and liquefied gas cargo water volume. Additionally, the possibility of using LSTM for predicting not only container port water volume but also petroleum and liquefied gas cargo water volume was confirmed, and it is expected to be applicable to future generalized studies through further research.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.8
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• pp.870-876
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• 2005

The multiple regression models which have two factors(population and commercial area) have been used to forecast the water demand in the future. But, the coefficient of population had a negative value because proper regional classification wasn't performed, and it is not reasonable because the population must be a positive factor. So, the regional classification was performed by principal component and cluster analysis to solve the problem. 6 regional characters were transformed into 4 principal components, and the areas were divided into two groups according to cluster analysis which had 4 principal components. The new regression models were made by each group, and the problem was solved. And, the future water demands were estimated by three scenarios(Active, moderate, and passive one). The increase of water demand ore $89.034\;m^3/day$ in active plat $49,077\;m^3/day$ in moderate plan, and $19,996\;m^3/day$ in passive plan. The water supply ability as scenarios is enough in water treatment plant, however, 2 reservoirs among 4 reservoirs don't have enough retention time in all scenarios.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.75-84
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• 2023

Many developing countries face challenges in estimating long-term discharge due to the lack of hydrological data for water supply planning, making it difficult to establish a rational water supply plan for decision-making on water distribution. The study area, the Bandung region in Indonesia, is experiencing rapid urbanization and population concentration, leading to a severe shortage of freshwater. The absence of water reservoir prediction methods has resulted in a water supply rate of approximately 20%. In this study, we aimed to propose an approach for predicting water reservoirs in developing countries by analyzing water safety and potential water supply using the MODSIM (Modified SIMYLD) network model. To assess the suitability of the MODSIM model, we applied the unit hydrograph method to calculate long-term discharge based on 19 years of discharge data (2002-2020) from the Pataruman observation station. The analysis confirmed alignment with the existing monthly optimal operation curve. The analysis of power plant capacity revealed a difference of approximately 0.30% to 0.50%, and the water intake safety at the Pataruman point showed 1.64% for Q95% flow and 0.47% for Q355 flow higher. Operational efficiency, compared to the existing reservoir optimal operation curve, was measured at around 1%, confirming the potential of using the MODSIM network model for water supply evaluation and the need for water supply facilities.