• Journal of The Korean Society of Agricultural Engineers
• /
• v.52 no.6
• /
• pp.63-73
• /
• 2010

This study is to develop a downstream flood damage prediction model for efficient confrontation in case of extreme and flash flood by future probable small agricultural dam break situation. For a Changri reservoir (0.419 million $m^3$) located in Yongin city of Gyeonggi province, a dam break scenario was prepared. With the probable maximum flood (PMF) condition calculated from the probable maximum precipitation (PMP), the flood condition by dam break was generated by using the HEC-HMS (Hydrologic Engineering Center - Hydrologic Modeling System) model. The flood propagation to the 1.12 km section of Hwagok downstream was simulated using HEC-RAS (Hydrologic Engineering Center - River Analysis System) model. The flood damaged areas were generated by overtopping from the levees and the boundaries were extracted for flood damage prediction, and the degree of flood damage was evaluated using IDEM (Inundation Damage Estimation Method) by modifying MD-FDA (Multi-Dimensional Flood Damage Analysis) and regression analysis simple method. The result of flood analysis by dam-break was predicted to occurred flood depth of 0.4m in interior floodplain by overtopping under PMF scenario, and maximum flood depth was predicted up to 1.1 m. Moreover, for the downstream of the Changri reservoir, the total amount of the maximum flood damage by dam-break was calculated nearly 1.2 billion won by IDEM.

• The KIPS Transactions:PartB
• /
• v.18B no.5
• /
• pp.271-278
• /
• 2011

Storm and flood such as torrential rains and major typhoons has often caused damages on a large scale in Korea and damages from storm and flood have been increasing by climate change and warming. Therefore, it is an essential work to maneuver preemptively against risks and damages from storm and flood by predicting the possibility and scale of the disaster. Generally the research on numerical model based on statistical methods, the KDF model of TCDIS developed by NIDP, for analyzing and predicting disaster risks and damages has been mainstreamed. In this paper, we introduced the model for prediction of damage cost from storm and flood by the neural network algorithm which outstandingly implements the pattern recognition. Also, we compared the performance of the neural network model with that of KDF model of TCDIS. We come to the conclusion that the robustness and accuracy of prediction of damage cost on TCDIS will increase by adapting the neural network model rather than the KDF model.

• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.884-887
• /
• 2006

This paper focuses on minimizing flood damage in the Yeongdeok basin of South Korea by establishing a flood prediction model based on a geographic information system (GIS), remote sensing, and geomorphoclimatic instantaneous unit hydrograph (GcIUH) techniques. The GIS database for flash flood prediction was created using data from digital elevation models (DEMs), soil maps, and Landsat satellite imagery. Flood prediction was based on the peak discharge calculated at the sub-basin scale using hydrogeomorphologic techniques and the threshold runoff value. Using the developed flash flood prediction model, rainfall conditions with the potential to cause flooding were determined based on the cumulative rainfall for 20 minutes, considering rainfall duration, peak discharge, and flooding in the Yeongdeok basin.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.62 no.3
• /
• pp.95-107
• /
• 2020

The failures of the agricultural reservoirs that most have more than 50 years, have increased due to the abnormal weather and localized heavy rains. There are many studies on the prediction of damage from reservoir collapse, however, these referenced studies focused on evaluating reservoir collapse as single unit and applyed to one and two dimensional hydrodynamic model to identify the fluid flow. This study is to estimate failure probability of spillway, sliding, bearing capacity and overflowing targeting small and medium scale agricultural reservoirs. In addition, we calculate failure probability by complex mode. Moreover, we predict downstream flood damage by reservoir failure applying three dimensional hydrodynamic model. When the reservoir destroyed, the results are as follows; (1) the flow of fluid proceeds to same stream direction and to a lower slope by potential and kinetic energy; (2) The predicted damage in downstream is evaluated that damage due to building destruction is the highest.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.216-218
• /
• 2022

Natural disasters caused by abnormal climates are continuously increasing, and the types of natural disasters that cause the most damage are flood damage caused by heavy rains and typhoons. Therefore, in order to reduce flood damage, this paper proposes a system that can predict the water level, a major parameter of flood, in real time using LSTM and GRU. The input data used for flood prediction are upstream and downstream water levels, temperature, humidity, and precipitation, and real-time prediction is performed through the pre-trained LSTM-GRU model. The input data uses data from the past 20 hours to predict the water level for the next 3 hours. Through the system proposed in this paper, if the risk determination function can be added and an evacuation order can be issued to the people exposed to the flood, it is thought that a lot of damage caused by the flood can be reduced.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2004.11a
• /
• pp.389-392
• /
• 2004

A recent unusual change in the weather is formed as a localized heavy rain in a short time. This phenomenon has caused a flash flood, and flash floods extensively have damaged human lives many times. In large river's case, the extent of loss of lives and properties has been decreased through the flood warning system by flood control stations of each stream. However, the extent of damage in other small rivers has increased reversely. Therefore, it is necessary to establish a new flood warning system against flash floods instead of the existing flood warning system. It is a specific character that the damage from flash floods in mountain streams brings much more loss of lives than large river's flood. The purpose of this study is calculating the characteristic of flash floods in streams, analyzing topographical characteristics of water basin through applying GIS techniques with the calculation as mentioned above and researching what topographical conditions have influence on hydrological flash floods in water basin. The flash flood prediction model we used is made by GIUH (geomorphoclimatic instantaneous unit hydrograph) with hydrologic-topographical technology. As applying the flash flood prediction model, this is a procedure for calculating topographical information in basin: we made a topological data up out of database with utilizing GIS, and we also produced a DEM (digital elevation model) and used it as a topographical data for determining amount of flash floods.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.91-98
• /
• 2023

With the increasing flood damage by frequently localized heavy rains, flood prediction research are being conducted to prevent flooding damage in advance. In this paper, we present a machine-learning scheme for developing a flooding depth and location prediction model using real-time rainfall data. This scheme proposes a dataset configuration method using the data as input, which can robustly configure various rainfall distribution patterns and train the model with less memory. These data are composed of two: valid total data and valid local. The one data that has a significant effect on flooding predicted the flooding location well but tended to have different values for predicting specific rainfall patterns. The other data that means the flood area partially affects flooding refers to valid local data. The valid local data was well learned for the fixed point method, but the flooding location was not accurately indicated for the arbitrary point method. Through this study, it is expected that a lot of damage can be prevented by predicting the depth and location of flooding in a real-time manner.

• Journal of Wetlands Research
• /
• v.19 no.4
• /
• pp.459-469
• /
• 2017

The aim of this study is to improve Potential Flood Damage(PFD) that a flood risk assessment technique used in the National Water Resource Plan comprehensive plan for water resources, which is a top-level plan related to domestic water resources and Flood Risk Indices. Both methods are used to evaluate flood control risks. However, there is a problem of reliability because the problem of data utilization and the damage that occurred in a specific area are applied as an average concept. Therefore, this study improved the method for analysis by components and the flood inundation area was limited to flood damage area. Also, the improvement of the method and the application of the recently provided GIS data to the flood damage prediction area were proposed to improve the usability of the existing method. The existing analysis method and the improved method were applied to the test watershed by each case.

• Journal of Korea Water Resources Association
• /
• v.52 no.10
• /
• pp.753-761
• /
• 2019

Localized heavy storm is one of the major causes of flood damage in urban regions. According to the recent disaster statistics in South Korea, the frequency of urban flood is increasing more frequently, and the scale is also increasing. However, localized heavy storm is difficult to predict, making it difficult for local government officials to deal with floods. This study aims to construct a Flood risk matrix (FRM) using ensemble weather prediction data and to assess its applicability as a means of reducing damage by securing time for such urban flood response. The FRM is a two-dimensional matrix of potential impacts (X-axis) representing flood risk and likelihood (Y-axis) representing the occurrence probability of dangerous weather events. To this end, a regional FRM was constructed using historical flood damage records and probability precipitation data for basic municipality in Busan and Daegu. Applicability of the regional FRMs was assessed by applying the LENS data of the Korea Meteorological Administration on past heavy rain events. As a result, it was analyzed that the flood risk could be predicted up to 3 days ago, and it would be helpful to reduce the damage by securing the flood response time in practice.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.11
• /
• pp.1471-1476
• /
• 2021

Flood damage can cause floods or tsunamis, which can result in enormous loss of life and property. In this regard, damage can be reduced by making a quick evacuation decision through flood prediction, and many studies are underway in this field to predict floods using time series data. In this paper, we propose a CNN-based time series prediction model. A CNN-based water level prediction model was implemented using the river level and precipitation, and the performance was confirmed by comparing it with the LSTM and GRU models, which are often used for time series prediction. In addition, by checking the performance difference according to the size of the input data, it was possible to find the points to be supplemented, and it was confirmed that better performance than LSTM and GRU could be obtained. Through this, it is thought that it can be utilized as an initial study for flood prediction.