• Water for future
• /
• v.10 no.2
• /
• pp.66-78
• /
• 1977

• Water for future
• /
• v.29 no.5
• /
• pp.51-56
• /
• 1996

• Water for future
• /
• v.53 no.2
• /
• pp.65-71
• /
• 2020

• Journal of Korean Society of Water and Wastewater
• /
• v.34 no.1
• /
• pp.35-43
• /
• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.243-243
• /
• 2015

Low-flow simulation and forecasting is one of the emerging issues in hydrology due to the increasing demand of water in dry periods. Even though low-flow simulation and forecasting remains a difficult issue for hydrologists better simulation and earlier prediction of low flows are crucial for efficient water management. The UN has never stated that South Korea is in a water shortage. However, a recent study by MOLIT indicates that Korea will probably lack water by 4.3 billion m3 in 2020 due to several factors, including land cover and climate change impacts. The two main situations that generate low-flow events are an extended dry period (summer low-flow) and an extended period of low temperature (winter low-flow). This situation demands the hydrologists to concentrate more on low-flow hydrology. Korea's annual average precipitation is about 127.6 billion m3 where runoff into rivers and losses accounts 57% and 43% respectively and from 57% runoff discharge to the ocean is accounts 31% and total water use is about 26%. So, saving 6% of the runoff will solve the water shortage problem mentioned above. The main objective of this study is to present the hydrological modelling approach for low-flow simulation and forecasting using a model that have a capacity to represent the real hydrological behavior of the catchment and to address the water management of summer as well as winter low-flow. Two lumped hydrological models (GR4J and CAT) will be applied to calibrate and simulate the streamflow. The models will be applied to Seolmacheon catchment using daily streamflow data at Jeonjeokbigyo station, and the Nash-Sutcliffe efficiencies will be calculated to check the model performance. The expected result will be summarized in a different ways so as to provide decision makers with the probabilistic forecasts and the associated risks of low flows. Finally, the results will be presented and the capacity of the models to provide useful information for efficient water management practice will be discussed.

• Geomechanics and Engineering
• /
• v.29 no.3
• /
• pp.321-329
• /
• 2022

Tunnel site where high water pressure is applied, such as subsea tunnel, generally selects the shield TBM (Tunnel Boring Machine) to maintain the tunnel excavation face. The shield TBM has cutters installed, and the cutters wear out during the process of excavation, so it should be checked and replaced regularly. This is called CHI (Cutterhead Intervention). The conventional CHI under high water pressure is very disadvantageous in terms of safety and economics because humans perform work in response to high water pressure and huge water inflow in the chamber. To overcome this disadvantage, this study proposes a new method to dramatically reduce water pressure and water ingress by injecting an appropriate grout solution into the front of the tunnel face through the shield TBM chamber, called New Face Grouting Method (NFGM). The tunnel model tests were performed to determine the characteristics, injection volume, and curing time of grout solution to be applied to the NFGM. Model test apparatus was composed of a pressure soil tank, a model shield TBM, a grout tank, and an air compressor to measure the amount of water inflow into the chamber. The model tests were conducted by changing the injection amount of the grout solution, the curing time after the grout injection, and the water/cement ratio of grout solution. From an economic point of view, the results showed that the injection volume of 1.0 L, curing time of 6 hours, and water/cement ratio of the grout solution between 1.5 and 2.0 are the most economical. It can be concluded that this study has presented a method to economically perform the CHI under the high water pressure.

• Journal of Environmental Impact Assessment
• /
• v.21 no.5
• /
• pp.715-724
• /
• 2012

In this study, we simulated water temperature in the downstream according to withdrawal types of dam using EFDC model. Three scenarios were assumed as water was released from the surface layer, the middle layer, and the bottom layer at intervals of 10m depth. In case of the surface layer withdrawal, the water temperature rose from March and lowered gradually after it reached a peak in August. The middle and the bottom layers effluence temperatures were lower than the surface layer temperature by maximum $15.9^{\circ}C$(in July), but after September, temperature inversion appeared. It was advantageous for the surface layer withdrawal to decrease cold damage and fog in downstream area and was possible to the middle and the bottom layers withdrawal from August to September. However, the reliability of model should be improved by accumulating the real-time information of water temperature.

• Journal of Korean Society of Water and Wastewater
• /
• v.31 no.6
• /
• pp.511-519
• /
• 2017

Multi-regional water supply system, which installed for supplying multiple water demands, is characterized by large-sized, long-distance, tree-type layout. This system is vulnerable to long-standing service interruption when a pipe breaks is occurred. In this study, a numerical method is proposed to calculate drainage time that directly affects time of service interruption. To begin with, governing equations are formulated to embed the delayed drainage effect by the friction loss, and to resolve complicated connection of pipelines, which are derived from the continuity and energy equations. The nonlinear hydraulic equations are solved by using explicit time integration method and the Newton-Raphson method. The developed model is verified by comparing the result with analytical solution. Furthermore, the model's applicability is validated by the examples of pipelines in serial, in parallel, and complex layout. Finally, the model is utilized to suggest an appropriate actions to reduce the deviation of draining time in the C transmission line of the B multi-regional water supply system.

• Journal of Korean Society of Water and Wastewater
• /
• v.19 no.6
• /
• pp.738-748
• /
• 2005

Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.46 no.1
• /
• pp.3-13
• /
• 2004

For optimal irrigation reservoir operation during flood and normal period, a general and systematic policy is suggested to make balance of the conflicting purposes between water conservation and flood control. We developed Open Water Management Program (OWMP) with an open architecture to deal with newly arising upgrade problems for optimal management of irrigation reservoir. And we evaluated the applicability of OWMP to estimate daily runoff from an agricultural watershed including irrigation reservoirs, and analyzed behaviour of irrigation reservoirs as irrigation water requirements considering frequency analysis of reservoir storage and frequency analysis water requirements for effective management of reservoir. When we executed OWMP with data produced from an experimental field, IHP basins, the mean relative errors of application of daily runoff and irrigation water requirement were less than 5％. We also applied OWMP to a Seongju irrigation reservoir to simulate daily runoff, storage and water requirement from 1998 to 2002, and the mean model efficiency between measured and simulated value was 0.76. Our results based on the magnitude of relative errors and model efficiency of the model simulation indicate that the OWMP can be a tool nicely adapted to the effective water management of irrigation reservoir for beneficial water use and flood disaster management.