• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.1
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• pp.39-51
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• 2016

Extreme floods occur more often recently as the frequency of extreme storm events increase due to the climate change. Because the extreme flood exceeding the design flood can cause large-scale disasters, it is important to predict and prepare for the future extreme flood. Flood flow is affected by two main factors; rainfall and land use. To predict the future extreme flood, both changes in rainfall due to the climate change and land use should be considered. The objective of this study was to simulate the future design flood in the Hwangguji river watershed, South Korea. The climate and land use change scenarios were derived from the representative concentration pathways (RCP) 4.5 and 8.5 scenarios. Conversion of land use and its effects (CLUE) and hydrologic modelling system (HEC-HMS) models were used to simulate the land use change and design flood, respectively. Design floods of 100-year and 200-year for 2040, 2070, and 2100 under the RCP4.5 and 8.5 scenarios were calculated and analyzed. The land use change simulation described that the urban area would increase, while forest would decrease from 2010 to 2100 for both the RCP4.5 and 8.5 scenarios. The overall changes in design floods from 2010 to 2100 were similar to those of probable rainfalls. However, the impact of land use change on design flood was negligible because the increase rate of probable rainfall was much larger than that of curve number (CN) and impervious area.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.620-624
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• 2010

The Integrated Flood Management (IFM) concept intends to obtain the maximum of benefits from the use of floodplains and the minimum of losses from flooding which seems to be the ideal outcome of flood management. For this, different strategies are recommended in order to reach this objective. The implementation of this concept leads to a resilience approach for the adaptive management of floods and is currently on its way to be better understood and integrated as part of the water resources management. There exist a fair amount of strategies to adapt to flooding that have been recommended or already implemented either as planned or autonomous ways of adaptation but their effectivity depends on the specific case where they are applied. This is why this paper looks to give a better overview of the different recommended or implemented strategies by analyzing them regarding their inherent advantages and disadvantages.

• Journal of Environmental Science International
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• v.29 no.9
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• pp.871-883
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• 2020

Due to recent heavy rain events, there are increasing demands for adapting infrastructure design, including drainage facilities in urban basins. Therefore, a clear definition of urban rainfall must be provided; however, currently, such a definition is unavailable. In this study, urban rainfall is defined as a rainfall event that has the potential to cause water-related disasters such as floods and landslides in urban areas. Moreover, based on design rainfall, these disasters are defined as those that causes excess design flooding due to certain rainfall events. These heavy rain scenarios require that the design of various urban rainfall facilities consider design rainfall in the target years of their life cycle, for disaster prevention. The average frequency of heavy rain in each region, inland and coastal areas, was analyzed through a frequency analysis of the highest annual rainfall in the past year. The potential change in future rainfall intensity changes the service level of the infrastructure related to hand-to-hand construction; therefore, the target year and design rainfall considering the climate change premium were presented. Finally, the change in dimensional safety according to the RCP8.5 climate change scenario was predicted.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.13-23
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• 2021

This study intended to assess the reliability of topographic data using satellite imaging data. The topographical data using actual instrumentation data and satellite image data were established and applied to the rainfall-leak model, S-RAT, and the topographical data and outflow data were compared and analyzed. The actual measurement data were collected from the Water Resources Management Information System (WAMIS), and satellite image data were collected from MODIS observation sensors mounted on Terra satellites. The areas subject to analysis were selected for two rivers with more than 80% mountainous areas in the Han River basin and one river basin with more than 7% urban areas. According to the analysis, the difference between instrumentation data and satellite image data was up to 50% for peak floods and up to 17% for flood totals in rivers with high mountains, but up to 13% for peak floods and up to 4% for flood totals. The biggest difference in the video data is Landuse, which shows that MODIS satellite images tend to be recognized as cities up to 60% or more in urban streams compared to WAMIS instrumentation data, but MODIS satellite images are found to be less than 5% error in forest areas.

• Journal of the Korean Society of Hazard Mitigation
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• v.5 no.2 s.17
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• pp.37-44
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• 2005

It reflects well feature of slope that is characteristic of urban river basin of Busan local. In this study, process various hydrological data and basin details data which is collected through basin basis data, hydrological monitoring system(EMS-DEU) and automatic water level equipment(AWS-DEU) for urban flood disaster prevention and use as basin input data of ILLUDAS, SWMM and HEC-HMS in order to examine outflow feature of experiment basin and then use in reservoir design of experiment basin through calibration and verification about HEC-HMS. Inserted design rainfall for 30 years that is design criteria of creek into HEC-HMS and then calculated design floods according to change aspect of the impermeable rate. Capacity of reservoir was determined on the outflow mass curve. Designed detention pond(volume $54,000m^3$) at last outlet upper stream of experiment basin, after designing reservoir. It could be confirmed that the peak flow was reduced resulting from examining outflow aspect. Designing reservoir must decrease outflow of urban areas.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.396-396
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• 2015

Frequent urban floods affect the human safety and economic properties due to a lack of the capacity of drainage system and the increased frequency of torrential rainfall. The drainage system has played an important role in flooding control, so it is necessary to establish the effective countermeasures considering the connection between drainage system and surface flow. To consider the connection, we selected SWMM5 model for analyzing transportation capacity of drainage system and FLUMEN model for calculating inundation depth and time variation of inundation area. First, Thiessen method is used to delineate the sub-catchments effectively base on drainage network data in SWMM5. Then, the output data of SWMM5, hydrograph of each manhole, were used to simulate FLUMEN to obtain inundation depth and time variation of inundation area. The proposed method is applied to Sadang area for the event occurred in $27^{th}$ of July, 2011. A total of 11 manholes, we could check the overflow from the manholes during that event as a result of the SWMM5 simulation. After that, FLUMEN was utilized to simulate overland flow using the overflow discharge to calculate inundation depth and area on ground surface. The simulated results showed reasonable agreements with observed data. Through the simulations, we confirmed that the main reason of the inundation was the insufficient transportation capacities of drainage system. Therefore cooperation of both models can be used for not only estimating inundation damages in urban areas but also for providing the theoretical supports of the urban network reconstruction. As a future works, it is recommended to decide optimized pipe diameters for efficient urban inundation simulations.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.84-93
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• 2020

In recent years, the scale of damage from disasters such as earthquakes and large-scale fires and floods that are occurring in Korea is increasing. Accordingly, interest in urban disaster prevention that combines living infrastructure such as roads and parks is boosting, and it is urgent to prepare measures to reduce the damage scale of local cities. The purpose of this study is to derive implications for disaster prevention measures in areas where disaster prevention safety of local cities is weak through examples of disaster prevention safety of local cities in case of disaster. To this end, this study analyzed the regional characteristics, current status, and disaster prevention problems of regional cities in Japan, and selected disaster-vulnerable areas, and considered the distance relationship between disaster prevention bases through road network analysis. In addition, road closure simulation using ArcGIS Network Analyst was conducted to analyze disaster prevention safety in the area. As a result, the situation of the village which has a high possibility of isolation by natural disasters was grasped in advance. Through this, the suburbs confirmed the necessity of supplementing the disaster prevention function through transportation maintenance such as forest roads, and it was found that the city needs to prepare a risk management system. Furthermore, this study suggests the need for research on areas with a high possibility of isolation, especially in areas where disaster prevention functions are weak in local cities in case of disaster, and shows countermeasures for disaster prevention measures and resident education.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.191-201
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• 2018

Recently, the frequency of heavy rainfall is increasing due to the effects of climate change, and heavy rainfall in urban areas has an unexpected and local characteristic. Floods caused by localized heavy rains in urban areas occur rapidly and frequently, so that life and property damage is also increasing. It is crucial how fast and precise observations can be made on successful flood management in urban areas. Local heavy rainfall is predominant in low-level storms, and the present large-scale radars are vulnerable to low-level rainfall detection and observations. Therefore, it is necessary to introduce a new urban flood forecasting system to minimize urban flood damage by upgrading the urban flood response system and improving observation and forecasting accuracy by quickly observing and predicting the local storm in urban areas. Currently, the WHAP (Water Hazard Information Platform) Project is promoting the goal of securing new concept water disaster response technology by linking high resolution hydrological information with rainfall prediction and urban flood model. In the WHAP Project, local rainfall detection and prediction, urban flood prediction and operation technology are being developed based on high-resolution small radar for observing the local rainfall. This study is expected to provide more accurate and detailed urban flood warning system by enabling high-resolution observation of urban areas.