• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.156-156
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• 2019

In this study, a gated recurrent unit (GRU) network is constructed based on a deep neural network (DNN) with the aim of restoring the missing daily flow data in river basins. Lai Chau hydrological station is located upstream of the Da river basin (Vietnam) is selected as the target station for this study. Input data of the model are data on observed daily flow for 24 years from 1961 to 1984 (before Hoa Binh dam was built) at 5 hydrological stations, in which 4 gauge stations in the basin downstream and restoring - target station (Lai Chau). The total available data is divided into sections for different purposes. The data set of 23 years (1961-1983) was employed for training and validation purposes, with corresponding rates of 80% for training and 20% for validation respectively. Another data set of one year (1984) was used for the testing purpose to objectively verify the performance and accuracy of the model. Though only a modest amount of input data is required and furthermore the Lai Chau hydrological station is located upstream of the Da River, the calculated results based on the suggested model are in satisfactory agreement with observed data, the Nash - Sutcliffe efficiency (NSE) is higher than 95%. The finding of this study illustrated the outstanding performance of the GRU network model in recovering the missing flow data at Lai Chau station. As a result, DNN models, as well as GRU network models, have great potential for application within the field of hydrology and hydraulics.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.656-664
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• 2023

Purpose: The Ministry of Employment and Labor manages disasters based on the standard manual for risk management of large-scale human accidents in workplaces when large-scale disasters such as fires and collapses occur in workplaces. We are going to check the standard manual currently in operation and suggest improvement plans for the insufficient items. Method: Accordingly, the standard manual was checked together with internal and external experts in the disaster management manual and disaster management staff at headquarters and local government offices, and items to be improved were identified with priority. Result: In case of a collapse accident, it is necessary for the Ministry of Public Administration and Security to accurately present the selection criteria in order to eliminate the controversy over the selection of the disaster management department. In addition, it seems necessary to supplement the details of the disaster safety communication network operation and evacuation guidelines. Conclusion: In the future, in order to improve the disaster management system that meets the public's eye level, it is expected to prepare a standard manual for risk management of large-scale human accidents in workplaces that guarantees the lives and safety of workers through the collection of opinions from experts in the relevant field, disaster management personnel, and the general public.

• Journal of the Korean Professional Engineers Association
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• v.41 no.2
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• pp.51-58
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• 2008

In this paper is described "the water resources policy, a new paradigm of water resources control. the characteristic of rainfall in Korea and the compatibility of a new water routes opening in natural river". Also the construction of the Korean grand waterway in the view of disaster prevention, flood control, irrigation and environment-friendly transportation is needed eventually. And I detail a routes of Korean grand waterway, using of a flood gate, supply of irrigation water. cruse duration, activating under developed region and economic efficiency.

• Journal of Coastal Disaster Prevention
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• v.5 no.4
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• pp.173-182
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• 2018

As the research about increasing the efficiency of dredging soil transport, the technology, which reduce the friction between pipe wall and fluid in the pipe and disturbed generating pipe blockage, has been developed. So for the purpose of applying this technology to real construction site, main test has been tried at the real scale test in field. As a test result, this paper will show 30% flow efficiency increasing by permitted electro magnetic force to the pipe. And test result was evaluated as a ultra sonic velocity profiler. To propose the design technique and the execution manual of the high efficiency dredging material transport technic, this research have confirmed flow status changing depending on a soil material kind under electro-magnetic field and analyze the effect of electro-magnetic field which affects to each dredged soil material transportation. For achieving this research, EMF(Electro-Magnetic Field) generator is installed on the dredger(20,000HP) and through monitored flow status, dredging soil flow rate and sampled material specification is confirmed. Also dredger operating condition is measured and dredger power for soil transportation, hydraulic gradient and flow rate are compared, as transportation efficiency is calculated by this parameter, it is possible to check transportation efficiency improvement depending on each dredged soil material under electro-magnetic field. To verify the technique of dredged soil transfer using electromagnetic field, which is the core technique of the high efficiency dredged soil transfer, and the technique of expert system for pipeline transfer and the flow state. This could lead to a verification of transfer efficiency according to the characteristics of the dredged soil (sand, clay, silt) and the transfer distance (5km, 10km, 15km), which is planned to be used for a technology development of pump power reduction and long-distance transfer applying the high efficiency dredged soil transfer technology.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.477-483
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• 2010

This paper is studied on a novel buck-boost isolated converter for high efficiency and high power factor. The switching devices in the proposed converter are operated by soft switching technique using a new quasi-resonant circuit, and are driven with discontinuous conduction mode (DCM) according to pulse width modulation (PWM). The quasi-resonant circuit makes use of a step up-down inductor and a loss-less snubber capacitor. The proposed converter with DCM also simplifies the requirement of control circuit and reduces a number of control components. The input ac current waveform in the proposed converter becomes a quasi sinusoidal waveform in proportion to the magnitude of input ac voltage under constant switching frequency. As a result, it is obtained by the proposed converter that the switching power losses are low, the efficiency of the converter is high, and the input power factor is nearly unity. The validity of analytical results is confirmed by some simulation results on computer and experimental results.

• Proceedings of the KIPE Conference
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• 2007.11a
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• pp.115-117
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• 2007

This paper is analyzed for DC-DC chopper performance of high efficiency added in electric isolation. The general converters of high efficiency are made that the power loss of the used switching devices is minimized. To achieve high efficiency system, the proposed chopper is constructed by using a partial resonant circuit. The control switches using in the chopper are operated with soft switching for a partial resonant method. The control switches are operated without increasing their voltage and current stresses by the soft switching technology. The result is that the switching loss is very low and the efficiency of chopper is high. And the proposed chopper is added in a electric isolation. When the power conversion system is required to electric isolation, the proposed chopper is adopted with system development of high efficiency. The soft switching operation and the system efficiency of the proposed chopper is verified by digital simulation and experimental results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.197-207
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• 2023

This study was conducted with the purpose of measuring the disaster damage reduction effect according to the provision of the early warning service ahead of the nationwide expansion. The damage reduction effect was measured using insurance data for 19 insured crops in areas that early warning services were provided during the period from 2017 to 2020. As a result of the measurement, it was analyzed that the early warning service had the effect of preventing or reducing disaster damage to farms. In particular, it was found that the disaster reduction effect was greater when disaster prevention facilities were equipped. The implications obtained from the results are as follows. First, by presenting subjectively experienced disaster reduction cases as numerical effects using insurance data with public confidence and objectivity, it can be used as basic data such as expansion of service area, discount of insurance premium with service adoption, and promotional materials for service subscription for early warning service. Second, in expanding and distributing early warning services, giving priority to areas or crops equipped with disaster prevention facilities can help increase the efficiency and effectiveness of the project.