• 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.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1248-1252
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• 1999

To provide electricity power of good quality, it is essential to establish generation of electricity plan in electric power system based on accurate power-demand prediction and cope with changes of power-need fluctuating constantly. The role of hydraulic-power generation of electricity in electric power system is of importance because responding to electric power-demand counts or reservoir-type hydraulic-power generation of electricity which is designed for additional load in electric power system. So hydraulic-power generation of electricity must have fast start reserve. But the amount of water, resources of reservoir-type hydraulic-power generation of electricity is restricted and multi-used, so the scheduling of management by exact forecasting the amount of water is critical. That is why efficient hydraulic-power generation of electricity makes a main role on pumping up the utility of energy and water resource. This thesis introduced the example of optimal generation of electricity plan establishment which is used in managing reservoir-type hydraulic-power generation of electricity.

• Magazine of the Korean Society of Agricultural Engineers
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• v.40 no.1
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• pp.68-77
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• 1998

The purpose of this paper is to establish a method estimating the daily urban water demand using statistical analysis that is used for developing the efficient management and operation of the water supply facilities, and accurary of the model is verified by error rate and F-value. The data used in this study were the daily urban water use, the weather conditions such as temperature, precipitation, relative humidity, etc, and the day of The week. The case study was taken placed for the city of Namwon in Korea. The raw data used in this study were rearranged either by month or by season for analysis purpose, and the statistical analysis was applied to the data to obtain the regression model As a result of this study, the linear regression model was developed to estimate the daily urban water use with weather condition. The regression constant and coefficients of the model were determined for each month of a year. The accuracy of the model was within 3% of average error and within 11% of maximum error. The resulting model was found to he useful to the practical operation and management of the water supply facilities.

• Journal of Environmental Impact Assessment
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• v.23 no.3
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• pp.177-186
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• 2014

Depending on the change of lifestyle and the improvement of people's living standards and rapid industrialization, urbanization of recent, demand for water is increasing rapidly. So emissions of domestic wastewater and various industrial waste water has increased, and water quality is worsening day by day. Therefore, in order to provide a measure against the occurrence of water pollution accident, this study was tried to simulate water pollution accident. This study simulated 2008 Gimcheon phenol accident using 1,2-D model, and analyze scenario for prevent of water pollution accident. Consequently the developed 1-D model presents high reappearance when compared with 2-D model, and has been able to obtain results in a short simulation run time. This study will contribute to the water pollution incident response prediction system and water quality analysis in the future.

• Journal of Korean Society of Rural Planning
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• v.23 no.3
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• pp.163-174
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• 2017

This study developed the prototype of the system and implemented its main functions, which is the intelligent integrated agricultural water management information system and service (IaWAMISS). The developed system was designed to be able to collect, process and analyze the agricultural water information of spatially dispersed reservoirs in whole country and spatial geographic information distributed in various systems of other organizations. The system, IaWAMISS, is also possible to provide the reproduced information services in each reservoir and space units, such as agricultural water demand and supply analysis and drought prediction, to the people, experts, and policy makers. This study defined the 6 step modules to develop the system, which are to design the components of intelligent integrated information system, to derive the utilization contents of existing systems, to design the new development elements for IaWAMISS, to design the reservoir information system can be used by managers of city and county, to designate the monitoring reservoirs managed by city and county, and finally to prepare the sharing system between organizations with the existing information systems. In order to implement the prototype of the system, this study shows the results for three important functions of the system: spatial integration of reservoirs' information, data link integration between the existing systems, and intelligent analysis program development to assist decision support for agricultural water management. For the spatial integration with the reservoir water information of the Korea Rural Community Corporation, this study get IaWAMISS to receive the real-time reservoir storage information from the measurement facility installed in the municipal management reservoir. The data link integration connecting databases of the existing systems, was implemented by integrating the meteorological information of the Korea Meteorological Administration with IaWAMISS, so that the rainfall forecast data could be derived and used. For the implementation of the intelligent analysis program, this study also showed the results of analysis and prediction of agricultural water demand and supply amount, estimation of Palmer drought index, analysis of flood risk area in typhoon course region, and analysis of the storage status of reservoirs related to each storm. This study confirmed the possibility and efficiency of an useful system development through the prototype design and implementation of IaWAMISS. By solving the preliminary 6 step modules presented in this study, it is possible not only to efficiently manage water by spatial unit, but also to provide the service of information and to enhance the relevant policy and national understanding to the people.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.4
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• pp.289-296
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• 2017

Pulp and paper industry produces large volumes of wastewater and residual sludge waste, resulting in many issues in relation to wastewater treatment and sludge disposal. Contaminants in pulp and paper wastewater include effluent solids, sediments, chemical oxygen demand (COD), and biological oxygen demand (BOD), which should be treated by wastewater treatment processes such as coagulation and biological treatment. However, few works have been attempted to predict the treatment efficiency of pulp and paper wastewater. Accordingly, this study presented empirical models based on experimental data in laboratory-scale coagulation tests and compared them with statistical models such as artificial neural network (ANN). Results showed that the water quality parameters such as turbidity, suspended solids, COD, and UVA can be predicted using either linear or expoential regression models. Nevertheless, the accuracies for turbidity and UVA predictions were relatively lower than those for SS and COD. On the other hand, ANN showed higher accuracies than the emprical models for all water parameters. However, it seems that two kinds of models should be used together to provide more accurate information on the treatment efficiency of pulp and paper wastewater.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.327-335
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• 2019

This study develops a prediction model for toilet and laundry water end-uses based on surveyed data which measured housing and household characteristics of 140 households over 5 years in Korea. Classical regression model assuming a normal distribution was not appropriate and estimated parameters were biased, because the distribution of measured water-uses was left-skewed. As an alternative to this problem, we considered the distribution of weibull and lognormal for each water-uses, and three regression models were compared using log-likelihood and scale parameter. As a result, weibull regression were chosen to be appropriate for both water-uses and also presented the factors that affect each water-use. This results expect that an insight is provided on water resources utilization and theoretical support role for effective water resource management.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.90-99
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• 2004

Stone column is one of the soft ground improvement method, which enhances ground conditions through ground water draining, settlement reducing and bearing capacity increasing complexly by using crushed stone instead of sand in general vertical drain methods. In recent, general construction material, sand is in short of supply, because of the unbalance of demand and supply. Also, the bearing capacity improving effect of stone column method is needed in many cases so the bearing capacity estimation is considered as important point. Nevertheless, adequate estimation methods to predict bearing capacity of stone column considering stone column and improving ground behavior reciprocally is not yet prepared. To contribute this situation, bearing capacity behavior of stone column were simulated as numerically on various property cases of crushed stone and surrounded ground. Through the numerical analysis of simulation results, bearing capacity behavior prediction formula was suggested. This formula was verified by comparing the prediction result with in situ test.

• Journal of Korea Water Resources Association
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• v.46 no.10
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• pp.969-976
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• 2013

Leakage in water distribution system causes social and economic losses by direct water loss into the ground, and additional energy demand for water supply. This research suggests a leak detection model of using adaptive Kalman filtering on real-time data of pipe flow. The proposed model takes into account hourly and daily variations of water demand. In addition, the model's prediction accuracy is improved by automatically calibrating the covariance of noise through innovation sequence. The adaptive Kalman filtering shows more accurate result than the existing Kalman method for virtual sine flow data. Then, the model is applied to data from two real district metered area in JE city. It is expected that the proposed model can be an effective tool for operating water supply system through detecting burst leakage and abnormal water usage.