• Disaster Prevention Review
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• v.13 no.4
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• pp.156-157
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• 2011

• Disaster Prevention Review
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• v.15 no.2
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• pp.88-89
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• 2013

• Disaster Prevention Review
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• v.15 no.2
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• pp.86-87
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• 2013

• Disaster Prevention Review
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• v.15 no.2
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• pp.82-83
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• 2013

• Disaster Prevention Review
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• v.15 no.2
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• pp.84-85
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• 2013

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.51-59
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• 2021

Due to recent climate change, the flood damage is becoming larger due to the development of localized heavy rains. 2020.12 The Ministry of Environment provides 100-year flood flood map, but in the case of small rivers, river structures are designed at 50-80 years frequency, making it difficult to predict damage and provide evacuation information. This study prepared flood map of Donamcheon district in Geumnam-myeon, Sejong Special Self-Governing Province, which is a small stream and habitual flood zone. The flood level was calculated using HEC-RAS and the flood area was visualized through HEC-GeoRAS. The analysis results showed that property damage such as special crops and roads occurred during the 30-80 year frequency rainfall, and it affected private houses such as general residential areas and public land when the frequency occurred for 100 years. The results of the comparison and analysis of the flood map provided by the Ministry of Environment and the results of the HEC-GeoRAS simulation showed that the flood map provided by the Ministry of Environment did not consider small streams. Further studies on flood flood maps considering the large and small stream are needed in the future.

• 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.).

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.191-191
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• 2017

Floods have become more widespread and frequent among natural disasters and consisted significant losses of lives and properties worldwide. Flood's impacts are threatening socio-economic and people's lives in the Mekong River Basin every year. The objective of this study is to identify the flood hazard areas and inundation depth in the Mekong River Basin. A rainfall-runoff and flood inundation model is necessary to enhance understanding of characteristic of flooding. Rainfall-Runoff-Inundation (RRI) model, a two-dimensional model capable of simulating rainfall-runoff and flood inundation simultaneously, was applied in this study. HydoSHEDS Topographical data, APPRODITE precipitation, MODIS land use, and river cross section were used as input data for the simulation. The Shuffled Complex Evolution (SCE-UA) global optimization method was integrated with RRI model to calibrate the sensitive parameters. In the present study, we selected flood event in 2000 which was considered as 50-year return period flood in term of discharge volume of 500 km3. The simulated results were compared with observed discharge at the stations along the mainstream and inundation map produced by Dartmouth Flood Observatory and Landsat 7. The results indicated good agreement between observed and simulated discharge with NSE = 0.86 at Stung Treng Station. The model predicted inundation extent with success rate SR = 67.50% and modified success rate MSR = 74.53%. In conclusion, the RRI model was successfully used to simulate rainfall runoff and inundation processes in the large scale Mekong River Basin with a good performance. It is recommended to improve the quality of the input data in order to increase the accuracy of the simulation result.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.10a
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• pp.55-61
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• 2004

In this study, we tried to develop the Web-GIS system prototype that will be able to effectively manage the nationwide flood prevention operations and to establish a framework that will be able to maintain the operation consistency. To achieve the study goals, we analysed current system of flood prevention operations, gathered related documents, had interviews with many government employees, and developed the Web-GIS system prototype. Also, we tried to present the benefit-cost analysis method using GIS technique that will be used to decide the priority order of the operation.

• 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.