• Proceedings of the Korea Water Resources Association Conference
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• 1995.05a
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• pp.42-47
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• 1995

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.879-889
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• 2014

In this study, to estimate loss of life considered flood characteristics using the relationship derived from analysis of historical dam break cases and the factors determining loss of life, the loss of life module applying in LIFESim and loss of life estimation by means of a mortality function were suggested and applicability for domestic dam watershed was examined. The flood characteristics, such as water depth, flow velocity and arrival time were simulated by FLDWAV model and flood risk area were predicted by using inundation depth. Based on this, the effects of warning, evacuation and shelter were considered to estimate the number of people exposed to the flood. In order to estimate fatality rates based on the exposed population, flood hazard zone is assigned to three different zones. Then, total fatality numbers were predicted after determining lethality or mortality function for each zone. In the future, the prediction of loss of life due to dam break floods will quantitatively evaluate flood risk and employ to establish flood mitigation measures at downstream applying probabilistic flood scenarios.

• Journal of Wetlands Research
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• v.19 no.4
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• pp.459-469
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• 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 Society of Waste Management
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• v.35 no.8
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• pp.777-784
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• 2018

Since climate change increases the risk of extreme rainfall events, concerns on flood management have also increased. In order to rapidly recover from flood damages and prevent secondary damages, fast collection and treatment of flood debris are necessary. Therefore, a quick and precise estimation of flood debris generation is a crucial procedure in disaster management. Despite the importance of debris estimation, methodologies have not been well established. Given the intrinsic heterogeneity of flood debris from local conditions, a regional-scale model can increase the accuracy of the estimation. The objectives of this study are 1) to identify significant damage variables to predict the flood debris generation, 2) to ascertain the difference in the coefficients, and 3) to evaluate the accuracy of the debris estimation model. The scope of this work is flood events in Ulsan city region during 2008-2016. According to the correlation test and multicollinearity test, the number of damaged buildings, area of damaged cropland, and length of damaged roads were derived as significant parameters. Key parameters seems to be strongly dependent on regional conditions and not only selected parameters but also coefficients in this study were different from those in previous studies. The debris estimation in this study has better accuracy than previous models in nationwide scale. It can be said that the development of a regional-scale flood debris estimation model will enhance the accuracy of the prediction.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.4
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• pp.1-17
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• 2009

In recent years, most of flood damage is associated with the levee failure. The objective of this study is to predict flow depths, flood area, flooding time and flood damage through flood inundation analysis considering the overflow of levee and the characteristics of levee failure. The hydrological parameters were extracted from GIS data such as DEM, land cover and soil map to estimate levee failure discharge. In addition, the characteristics of flood wave propagation could be accurately predicted as flood inundation analysis was accomplished considering the affection of structure within protected lowland and hourly prediction of flooded areas and estimation of flood strength will be utilized as basic data for the flood defence and establishment of measure to reduce flood damage.

• Journal of Wetlands Research
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• v.19 no.4
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• pp.423-432
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• 2017

This study used SSARR model to predict the flood hydrograph for the Miho stream in the Geum river basin. First, we performed the sensitivity analysis on the parameters of SSARR model to know the characteristics of the parameters and set the range. For the parameter calibration, optimization methods such as genetic algorithm, pattern search and SCE-UA were used. WSSR and SSR were applied as objective functions, and the results of optimization method and objective function were compared and analyzed. As a result of this study, flood prediction was most accurate when using pattern search as an optimization method and WSSR as an objective function. If the parameters are optimized based on the results of this study, it can be helpful for decision making such as flood prediction and flood warning.

• Journal of Industrial Technology
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• v.19
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• pp.369-377
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• 1999

The determination of the design flood based on probabilistic concepts is one of the important matters of the general field of hydrology. Until now, Most of any existing formulas to predict the flood flow were estimated by very different values with each other when we applied these formulas to the small basin, in extreme case, which were estimated over top be 400% of a difference because these have been developed by foreigners or derived from the big basin. The objective of this thesis is to examine closely the characteristics of frequency flood flow for reliable prediction of the flood flow through the probabilistic method in hydrology and to develop the ($Q_T=27.74T^{0.178}A^{0.594}$) applicable to the river of Kangwon province.

• Water for future
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• v.18 no.3
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• pp.265-270
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• 1985

The prediction of a design-flood hydrograph at a particular site on a river may be based on the derivation of discharge or stage hydrograph at an upstream section, togeater with a method to route this hydrograph along the rest of river. On the other hand, flood routing methods provide a useful tool for the analysis of flooding in all but the smaller catchment, and these methods are largely stored into hydrological method and hydraulic method. Although the Muskingum Method as a hydrological method ignores dynamic effects on the flood wave, Muskingum-Cunge Method based on hydraulic method is possible to improve the method so that it gives a good approximation to the solution of the linear convective-diffusion equation. This is made on the basis of the finite diffeience equation for the Muskingum Method. In the study, the outflows predicted by Muskingum-Cunge Method are campared with the observed outflows of the Pyung Chang River.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.839-850
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• 2018

The flood damage in urban areas due to torrential rain is increasing with urbanization. For this reason, accurate and rapid flooding forecasting and expected inundation maps are needed. Predicting the extent of flooding for certain rainfalls is a very important issue in preparing flood in advance. Recently, government agencies are trying to provide expected inundation maps to the public. However, there is a lack of quantifying the extent of inundation caused by a particular rainfall scenario and the real-time prediction method for flood extent within a short time. Therefore the real-time prediction of flood extent is needed based on rainfall-runoff-inundation analysis. One/two dimensional model are continued to analyize drainage network, manhole overflow and inundation propagation by rainfall condition. By applying the various rainfall scenarios considering rainfall duration/distribution and return periods, the inundation volume and depth can be estimated and stored on a database. The Rainfall-Duration-Flooding Quantity (RDF) relationship curve based on the hydraulic analysis results and the Self-Organizing Map (SOM) that conducts unsupervised learning are applied to predict flooded area with particular rainfall condition. The validity of the proposed methodology was examined by comparing the results of the expected flood map with the 2-dimensional hydraulic model. Based on the result of the study, it is judged that this methodology will be useful to provide an unknown flood map according to medium-sized rainfall or frequency scenario. Furthermore, it will be used as a fundamental data for flood forecast by establishing the RDF curve which the relationship of rainfall-outflow-flood is considered and the database of expected inundation maps.