• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.797-808
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• 2019

This study evaluated the applicability of spacebourne datasets to the flood analysis in an ungauged watershed where is no discharge measurements. The Duman River basin of North Korea was selected as a target area which was flooded by recent Typhoon Lionrock. Topographical parameters for flood analysis were estimated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM). GDEM includes the shortcomings of information on river cross-section, and conducted 2 dimensional flood analysis when considering virtual river cross-section and not considering it. As a result of comparative analysis, an error occurs in the inundation area and depth, but when used carefully, it is considered that the satellite image can be used for creating flood hazard map and utilizing information for response and preparation.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.182-182
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• 2017

Many small island nations rely on groundwater as their only other source of freshwater in addition to rainwater harvesting. The volume of groundwater resource of small island nations are further limited by their smaller surface area and specific hydrogeology. The rapid growth of population and tourism has led to increasing water demands and pollution of available groundwater resources. The predicted climate change effects pose significant threats to the already vulnerable freshwater lens of small islands in the form of rise in sea level, coastal inundation, saltwater intrusion, varied pattern of precipitation leading to droughts and storm surges. The effects of climate change are further aggravated by manmade stresses like increased pumping. Thus small island water resources are highly threatened under the effects of climate change. But due to the limited technical and financial capacity most of the small island developing states were unable to conduct detailed technical investigations on the effects of climate change on their water resources. In this study, we investigate how well small island countries are preparing for climate change. The current state of freshwater resources, impacts of predicted climate change along with adaptation and management strategies planned and implemented by small island countries are reviewed. Proper assessment and management practices can aid in sustaining the groundwater resources of small islands under climate change.

• Journal of Ecology and Environment
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• v.43 no.3
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• pp.289-296
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• 2019

We investigated the mortality and the oxidative damages of Deschampsia antarctica in response to waterlogging stress. In field, we compared the changes in the density of D. antarctica tuft at the two different sites over 3 years. The soil water content at site 2 was 6-fold higher than that of site 1, and the density of D. antarctica tuft decreased significantly by 55.4% at site 2 for 3 years, but there was no significant change at site 1. Experimental results in growth chamber showed that the $H_2O_2$ and malondialdehyde content increased under root-flooding treatment (hypoxic conditions-deficiency of $O_2$), but any significant change was not perceptible under the shoot-flooding treatment (anoxic condition-absence of $O_2$). However, total chlorophyll, soluble sugar, protein content, and phenolic compound decreased under the shoot-flooding treatment. In addition, the catalase activity increased significantly on the 1st day of flooding. These results indicate that hypoxic conditions may lead to the overproduction of reactive oxygen species, and anoxic conditions can deplete primary metabolites such as sugars and protein in the leaf tissues of D. antarctica. Under present warming trend in Antarctic Peninsula, D. antarctica tuft growing near the shoreline might more frequently experience flooding due to glacier melting and inundation of seawater, which can enhance the risk of this plant mortality.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.259-266
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• 2023

Global climate change is increasing, and the damage and scale of localized torrential rains are increasing. Pre-flood analysis simulation results should be derived from rainfall data through rainfall forecasts to prevent flood damage. In addition, it is necessary to control the use and management of flood response disaster prevention facilities through immediate decision-making. However, methods using spills and flood models such as XPSWMM and GATE2018 are limited due to professional usability and complex analytical procedures. Prototype (flood disaster prevention facility simulator) of this study is developed by calculating rainfall (short-term and long-term) using CBD software development methods. It is also expected to construct administrator and user-centric interfaces and provide GIS and visible data (graphs, charts, etc.).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• The Journal of Engineering Geology
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• v.10 no.2
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• pp.36-51
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• 2000

Almost every year, Korean suffered from the repetitive natural disasters such as typhoons and floods. During last 10 years, Korea experienced annual average of $50 million property damages caused by inundation. To estabilish the flood disaster counter plan, knowledge for flood damage causes based on the field investigations of inundated area is required. The field investigations is focused on technique to document and analyze the meteorological conditions leading to torrential rains, the causes and patterns of flooding, the performance of flood control structures in affected areas, the extent damages and the effectiveness of local emergency response and recovery actions. We did comparative analysis of field investigation techniques. As a major goal of flood hazard map design, one of non structural disaster countermeasures, it was expected to reduce flood damage losses by requiring local governments to implement land-use regulation that would result in safe building practices in flood hazard areas. This requires local governments to develop flood hazard maps to assess how to manage particularly vulnerable floodplain areas. In this study we suggested a design manual and the management system of flood hazard map.

• Journal of the Korean Geographical Society
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• v.28 no.4
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• pp.298-311
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• 1993

Simulation modeling was applied to predict the response of northeast Florida coastal wetlands to futrue sea levl rise due to global warming. Remote sensing and geographic information systems (GLS) were used to develop, manipulate, and synthesize input data, including land cover, digital elevation data, and site characteristics data. The SLAMM3 model evaluated this input data to predict responses of coastal wetlands and lowlands to inundation and erosion by sea level rise, and determined transfers from one habitat to another on a cell-by-cell basis. Significant changes were predicted from different scenarios of sea level rise: 0.5m, 1.0m, and 1.25m. The simulations indicated that 31.9 percent and 40.0 percent of wetlands within the study area would be lost with 1.0m and 1.25m sea level rise respectively, and a 6.5 percent loss with 0.5m rise.

• The Journal of Engineering Geology
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• v.33 no.3
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• pp.489-502
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• 2023

Urban floods pose significant challenges to cities worldwide, driven by the interplay between urbanization and climate change. This review examines recent studies of urban floods to understand their causes, impacts, and potential mitigation strategies. Urbanization, with its increase in impermeable surfaces and altered drainage patterns, disrupts natural water flow, exacerbating surface runoff during intense rainfall events. The impacts of urban floods are far-reaching, affecting lives, infrastructure, the economy, and the environment. Loss of life, property damage, disruptions to critical services, and environmental consequences underscore the urgency of effective urban flood management. To mitigate urban floods, integrated flood management strategies are crucial. Sustainable urban planning, green infrastructure, and improved drainage systems play pivotal roles in reducing flood vulnerabilities. Early warning systems, emergency response planning, and community engagement are essential components of flood preparedness and resilience. Looking to the future, climate change projections indicate increased flood risks, necessitating resilience and adaptation measures. Advances in research, data collection, and modeling techniques will enable more accurate flood predictions, thus guiding decision-making. In conclusion, urban flooding demands urgent attention and comprehensive strategies to protect lives, infrastructure, and the economy.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.340-340
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• 2020

본 연구는 산지-도심-해안으로 연결된 해안도시의 지역적인 특성으로 인해 해일과 같은 조위 상승 원인이 침수 및 홍수 위험을 증가시키는 복합재난에 대응하기 위하여 실시간 침수 위험정보 확인 및 대응, 대시민 상황전파 등이 가능하도록 IoT 융합 침수 대응시스템을 개발하는 것이다. 또한, 침수 및 홍수방어벽 등의 구조적대책을 검토하여 실시간 IoT 융합 침수 대응시스템과 연계운영함으로써 해안도시의 시설 및 재산을 보호하고 시민의 안전을 확보하는데 기여할 수 있는 실증연구를 수행하는 것을 목표로 한다. 침수 대응을 위한 구조적 대책은 양산형 IoT 침수 및 하천수위 감지 장비, 센서 연동 침수 및 홍수방어벽, 복합재난알림 디스플레이 등의 개발이며, 비구조적 대책은 강우-하천수위-조위 실측자료기반의 머신러닝 침수위험 분석 모듈 개발, 수치모의에 의한 예상 침수심 및 침수위험지도 작성, 재난상황 전파 알림서비스 등이 있으며 개발된 시제품과 기술을 테스트베드에 적용하고 부산광역시에서 운영하는 재난상황관리시스템인 스마트빅보드(SBB, Smart Big Board)에 연계 및 탑재함으로써 향상된 침수대응 및 대시민 서비스가 가능한 시스템을 구축 및 실증하고자 한다. 센서, 통신, 자료연계 및 분석에 대한 IoT 융합 기술을 침수 대응 기술개발에 적극 활용하여 실측자료와 수치모의 분석 결과가 연계된 침수위험정보를 생산하고 실시간 대응이 가능한 효율적이고 실질적으로 피해를 저감 할 수 있는 기술을 개발하고 적용성을 입증한다면 국내외 해안도시에 확대 보급이 가능해져 침수 피해로부터 효과적으로 국민의 재산과 생명 보호할 수 있다. 또한, IoT 기술을 연계한 재난 모니터링 시스템의 확산으로 복합재난 대응 연구에 기여할 수 있고 상대적으로 실측자료 수집, 연구에 소외되어 온 소하천 등에도 적용이 가능하여 재난으로 인한 사회적 비용을 경감시킬 수 있을 것으로 기대된다.

• Journal of Korea Water Resources Association
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• v.49 no.6
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• pp.519-528
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• 2016

Frequently torrential rain is occurred by climate change and urbanization. Urban is formed with road, residential and underground area. Without detailed topographic flooded analysis consideration can take a result which are wrong flooded depth and flooded area. Especially, flood analysis error of population and assets in dense downtown is causing a big problem for establishments and disaster response of flood measures. It can lead to casualties and property damage. Urban flood analysis is divided into sewer flow analysis and surface inundation analysis. Accuracy is very important point of these analysis. In this study, to confirm the effects of the elevation data precision in the process of flooded analysis were studied using 10m DEM, LiDAR data and 1:1,000 digital map. Study area is Dorim-stream basin in the Darim drainage basin, Sinrim 3 drainage basin, Sinrim 4 drainage basin. Flooding simulation through 2010's heavy rain by using XP-SWMM. Result, from 10m DEM, shows wrong flood depth which is more than 1m. In particular, some of the overflow manhole is not seen occurrence. Accordingly, detailed surface data is very important factor and it should be very careful when using the 10m DEM.