• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.659-663
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• 2010

In recent, flood inundation damages by hydraulic structure failures have increased drastically and thus a variety of countermeasures were needed to minimize such damages. A real-time flood inundation prediction technique is essential to protect and mitigate flood inundation damages. In the context of real time flood inundation modeling, this study aims to develop a grid based two-dimensional numerical method for flood inundation modeling using globally-available DEM data: SRTM with $90m{\times}90m$ spatial resolution. The newly-developed model guarantees computational efficiency in terms of geometric data processing by direct application of DEM for flood inundation modeling and also have good compatibility with various types of raster data when compared to a commercial model such as FLUMEN. The model, which employed the leap-frog algorithm to solve shallow water and continuity equations, can simulate inundating flow from channel to lowland and also returning flow from lowland to channel by comparing water levels between channel and lowland in real time. We applied the model to simulate the BaekSan levee break in the Nam river during a flood period from August 10 to 13, 2002. The simulation results had good agreements with the field-surveyed data in terms of inundated area and also showed physically-acceptable velocity vector maps with respect to inundating and returning flows.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.51-65
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• 2010

We suggest a simple and practical flood forecasting and warning system, which can predict change in the water level of a river in a small to medium-size watershed where flash flooding occurs in a short time. We first choose the flood defense target points, through evaluation of the flood risk of dike overflow and lowland inundation. Using data on rainfall, and on the water levels at the observed and prediction points, we investigate the interrelations and derive a regression formula from which we can predict the flood level at the target points. We calculate flood water levels through a calibrated flood simulation model for various rainfall scenarios, to overcome the shortage of real water stage data, and these results as basic population data are used to derive a regression formula. The values calculated from the regression formula are modified by the weather condition factor, and the system can finally predict the flood stages at the target points for every leading time. We also investigate the applicability of the prediction procedure for real flood events of the Jungnang Stream basin, and find the forecasting values to have close agreement with the surveyed data. We therefore expect that this suggested warning scheme could contribute usefully to the setting up of a flood forecasting and warning system for a small to medium-size river basin.

• Journal of Korean Society of Rural Planning
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• v.21 no.3
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• pp.171-179
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• 2015

The objectives of this research have been focussed on 1) developing prediction techniques for the flash flood and landslide based on rainfall prediction data in agricultural area and 2) developing an integrated forecasting system for the abrupt disasters using USN based real-time disaster sensing techniques. This study contains following steps to achieve the objective; 1) selecting rainfall prediction data, 2) constructing prediction techniques for flash flood and landslide, 3) developing USN and communication network protocol for detecting the abrupt disaster suitable for rural area, & 4) developing mobile application and SMS based early warning service system for local resident and tourist. Local prediction model (LDAPS, UM1.5km) supported by Korean meteorological administration was used for the rainfall prediction by considering spatial and temporal resolution. NRCS TR-20 and infinite slope stability analysis model were used to predict flash flood and landslide. There are limitations in terms of communication distance and cost using Zigbee and CDMA which have been used for existing disaster sensors. Rural suitable sensor-network module for water level and tilting gauge and gateway based on proprietary RF network were developed by consideration of low-cost, low-power, and long-distance for communication suitable for rural condition. SMS & mobile application forecasting & alarming system for local resident and tourist was set up for minimizing damage on the critical regions for abrupt disaster. The developed H/W & S/W for integrated abrupt disaster forecasting & alarming system was verified by field application.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.405-408
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• 2008

Up to now, a lot of houses, roads and other urban facilities have been damaged by natural disasters such as flash floods and landslides. It is reported that the size and frequency of disasters are growing greatly due to global warming. In order to mitigate such disaster, flood forecasting and alerting systems have been developed for the Han river, Geum river, Nak-dong river and Young-san river. These systems, however, do not help small municipal departments cope with the threat of flood. In this study, a real-time urban flood forecasting service (U-FFS) is developed for ubiquitous computing city which includes small river basins. A test bed is deployed at Tan-cheon in Gyeonggido to verify U-FFS. It is found that U-FFS can forecast the water level of outlet of river basin and provide real-time data through internet during heavy rain. Furthermore, it is expected that U-FFS presented in this study can be applied to ubiquitous computing city (u-City) and/or other cities which have suffered from flood damage for a long time.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.142-142
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• 2021

An arbitrary representation of an urban drainage sewer system was devised using a geographic information system (GIS) tool in order to calculate the surface and subsurface flow interaction for simulating urban flood. The proposed methodology is a mean to supplement the unavailability of systematized drainage system using high-resolution digital elevation(DEM) data in under-developed countries. A modified DEM was also developed to represent the flood propagation through buildings and road system from digital surface models (DSM) and barely visible streams in digital terrain models (DTM). The manhole, sewer pipe and storm drain parameters are obtained through field validation and followed the guidelines from the Plumbing law of the Philippines. The flow discharge from surface to the devised sewer pipes through the storm drains are calculated. The resulting flood simulation using the modified DEM was validated using the observed flood inundation during a rainfall event. The proposed methodology for constructing a hypothetical drainage system allows parameter adjustments such as size, elevation, location, slope, etc. which permits the flood depth prediction for variable factors the Plumbing law. The research can therefore be employed to simulate urban flood forecasts that can be utilized from traffic advisories to early warning procedures during extreme rainfall events.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5B
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• pp.267-272
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• 2012

To reduce the flood risk caused by unexpected heavy rainfall, many prediction methods for flood have been developed. A major constituent of flood prediction is an accurate rainfall estimation which is an input of hydrologic models. In this study, a regional-scale weather model which can provide relatively longer lead time for flood mitigation compared to the Nowcasting based on radar system will be introduced and applied to the Chongmi river basin located in central part of South Korea. The duration of application of a regional weather model is from July 11 to July 23 in 2006. The estimated rainfall amounts were compared with observations from rain gauges (Sangkeuk, Samjook, and Sulsung). For this rainfall event at Chongmi river basin, Thomson and Kain-Frisch Schemes for microphysics and cumulus parameterization, respectively, were selected as optimal physical conditions to present rainfall fall amount in terms of Mean Absolute Relative Errors (MARE>0.45).

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.29-29
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• 2023

To mitigate the damaging impacts of floods, accurate prediction of runoff, streamflow and flood inundation is needed. Conventional approach of simulating hydrology and hydraulics using loosely coupled models cannot capture the complex dynamics of surface and sub-surface processes. Additionally, the scarcity of data in ungauged basins and quality of data in gauged basins add uncertainty to model predictions, which need to be quantified. In this presentation, first the role of integrated modeling on creating accurate flood simulations and inundation maps will be presented with specific focus on urban environments. Next, the use of machine learning in producing streamflow predictions will be presented with specific focus on incorporating covariate shift and the application of theory guided machine learning. Finally, a framework to quantify the uncertainty in flood models using Hierarchical Bayesian Modeling Averaging will be presented. Overall, this presentation will highlight that creating accurate information on flood magnitude and extent requires innovation and advancement in different aspects related to hydrologic predictions.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.545-552
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• 2021

The burden of flood control on the dam under frequently flood due to climate change and especially heavy flood in 2020 year are come to the forward and increased. The objective of the study is therefore to establish the method to estimate capacity and target water level for flood control in actual dam management. Frequency matching method was applied to establish a pair of cumulative distribution function (CDF) based on daily dam inflow and discharge records. The relationship between dam storage and discharge volume represented as a percentage of inflow volume was derived and its characteristics was analyzed. As the result, the Soyanggang (45%) and Chungju Dam (39%) contributing to flood control with temporarily storing flood runoff. The method and diagram to estimate flood control capacity and target water level for flood control in the dam were established. The result of the study could be used as a supplementary data for flood control of the dam according to the rainfall prediction on the Korea Meteorological Administration.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.20-25
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• 2012

New Zealand suffers from regular floods, these being the most common source of insurance claims for damage from natural hazard events in the country. This paper describes the origin and distribution of the largest floods in New Zealand, and describes the systems used to monitor and predict floods. In New Zealand, broad-scale heavy rainfall (and flooding), is the result of warm moist air flowing out from the tropics into the mid-latitudes. There is no monsoon in New Zealand. The terrain has a substantial influence on the distribution of rainfall, with the largest annual totals occurring near the South Island's Southern Alps, the highest mountains in the country. The orographic effect here is extreme, with 3km of elevation gained over a 20km distance from the coast. Across New Zealand, short duration high intensity rainfall from thunderstorms also causes flooding in urban areas and small catchments. Forecasts of severe weather are provided by the New Zealand MetService, a Government owned company. MetService uses global weather models and a number of limited-area weather models to provide warnings and data streams of predicted rainfall to local Councils. Flood monitoring, prediction and warning are carried out by 16 local Councils. All Councils collect their own rainfall and river flow data, and a variety of prediction methods are utilized. These range from experienced staff making intuitive decisions based on previous effects of heavy rain, to hydrological models linked to outputs from MetService weather prediction models. No operational hydrological models are linked to weather radar in New Zealand. Councils provide warnings to Civil Defence Emergency Management, and also directly to farmers and other occupiers of flood prone areas. Warnings are distributed by email, text message and automated voice systems. A nation-wide hydrological model is also operated by NIWA, a Government-owned research institute. It is linked to a single high resolution weather model which runs on a super computer. The NIWA model does not provide public forecasts. The rivers with the greatest flood flows are shown, and these are ranked in terms of peak specific discharge. It can be seen that of the largest floods occur on the West Coast of the South Island, and the greatest flows per unit area are also found in this location.