• Journal of Korea Water Resources Association
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• v.38 no.3 s.152
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• pp.199-212
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• 2005

The study on the plan for rehabilitation project of domestic water distribution system - especially using Heuristic Algorithm as Genetic Algorithm which is expected to provide a more optimal solution effectively - has not been done sufficiently. The purpose of this study is the development of the optimal decision making system for the rehabilitation of the water distribution system considering economic and hydraulic influences using ReHS which is recent study of OR technique. Five different models with different objective functions are developed and tested to virtual pipe network according to various conditions considered in this study. These models provide more options for the rehabilitation of pipe network systems compared to previously suggested models in the literature.

• Journal of Energy Engineering
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• v.28 no.3
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• pp.55-64
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• 2019

This study identified key patents on U.S. underground water technology through patent network analysis. As a result, there were many technologies that used the technology to remove heavy metals to prevent contamination of groundwater. While patents between groundwater technology patents were in charge of intermediaries, the connectivity between groundwater technologies is not high. The patented technologies related to groundwater were largely distinguishable by pumping, monitoring, and decontamination. Monitoring includes techniques that enable identification of physical and biological properties, such as the type of contaminants, as well as geographic characteristics for analysis of groundwater flow, flow or water quality. Pollution purification technology refers to the process of physiochemical and biological purification for soil and groundwater. U.S. technology cases showed that the U.S. had high technology in water treatment area. And patent protection were also needed to cope with water shortages caused by climate change.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.334-341
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• 2004

The coefficient of permeability is one of the most important properties in unsaturated soils. The permeability varies with change in the water content as the soil water characteristic curve(SWCC) does. Thus the permeability curve of unsaturated soils has the similar shape with the soil-water characteristic curve(SWCC). Therefore, the methodologies have been studied to simply predict the unsaturated permeability from the SWCC. In this study, the experimental tests of SWCC and permeability were carried out for domestic weathered granite soils. The SWCC test results were fitted to Fredlund and Xing's SWCC equation and then it was found that there are some relationships between the parameters of SWCC equation and the basic soil properties. Accordingly we used an ANN(artificial neural network) model to obtain the SWCC parameters from the basic soil properties. Finally, the coefficients of permeability were predicted from these results by a prediction model.

• Computers and Concrete
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• v.19 no.5
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• pp.527-535
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• 2017

In this study, an aging deformation statistical model for a unique high and steep rock slope was proposed, and the aging characteristic of the slope deformation was better reflected. The slope displacement was affected by multiple-environmental factors in multiple scales and displayed the same tendency with a rising water level. The statistical model of the high and steep rock including non-aging factors was set up based on previous analyses and the study of the deformation and residual tendency. The rule and importance of the water level factor as a non-aging unit was analyzed. A partitioned statistical model and mutation model were established for the comprehensive cumulative displacement velocity with the monitoring study under multiple factors and multiple parameters. A spatial model was also developed to reflect and predict the whole and sectional deformation character by combining aging, deformation and space coordinates. A neural network model was built to fit and predict the deformation with a high degree of precision by mastering its feature of complexity and randomness. A three-dimensional finite element model of the slope was applied to approach the structure character using numerical simulations. Further, a three-dimensional finite element model of the slope and dam was developed, and the whole deformation state was analyzed. This study is expected to provide a powerful and systematic method to analyze very high, important and dangerous slopes.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.39-50
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• 2000

Generally speaking, a hydrodynamic model needs a friction coefficient (Manning coefficient or Chezy coefficient) and eddy viscosity. For numerical solution the coefficients are usually determined by recursive calculations. The eddy viscosity in numerical model plays physical diffusion in flow and also acts as numerical viscosity. Hence its value has influence on the stability of numerical solution and for these reasons a consistent evaluation procedure is needed. By using records of stage and discharge in the downstream reach of the Han river, I-D models (HEC-2 and NETWORK) and 2-D model (SMS), estimated values of Manning coefficient and an empirical equation for eddy viscosity are presented. The computed results are verified through the recorded flow elevation data.n data.

• 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 the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.957-968
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• 2021

To improve the data on reliability of the onshore fish farm water quality monitoring system and operate the system efficiently, the water quality data of the onshore seawater fish farms which are progressing test operation, and the marine environmental information network(Wando tidal station) were compared and analyzed. Furthermore, data validation, data range filters, and data displacement checks were applied to analyze the data in a way that eliminates the data errors in water quality monitoring systems and increases the reliability of measurement data.

• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.105-117
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• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

• KCID journal
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• v.2 no.1
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• pp.30-38
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• 1995

This paper presents three optimization analyses for constructing an irrigation pipe network for upland crop. A Sample network involving a pump and a storage tank is solved using the proposed three analyses. The first analysis is to resolve the sample netw

• Water Engineering Research
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• v.3 no.2
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.