• Journal of the Korean Association of Geographic Information Studies
• /
• v.11 no.3
• /
• pp.52-67
• /
• 2008

Recently, frequent occurrence of flash floods caused by climactic change has necessitated prompt and quantitative prediction of precipitation. In particular, the usability of rainfall radar that can carry out real-time observation and prediction of precipitation behavior has increased. Moreover, the use of distributed hydrological model that enables grid level analysis has increased for an efficient use of rainfall radar that provides grid data at 1km resolution. The use of distributed hydrologic model necessitates grid-type spatial data about target basins; to enhance reliability of flood runoff simulation, the use of visible and precise data is necessary. In this paper, physically based $Vflo^{TM}$ model and ModClark, a quasi-distributed hydrological model, were used to carry out flood runoff simulation and comparison of simulation results with data from Imjin River Basin, two-third of which is ungauged. The spatial scope of this study was divided into the whole Imjin River basin area, which includes ungauged area, and Imjin River basin area in South Korea for which relatively accurate and visible data are available. Peak flow and lag time outputs from the two simulations of each region were compared to analyze the impact of uncertainty in topographical parameters and soil parameters on flood runoff simulation and to propose effective methods for flood runoff simulation in ungauged regions.

• Journal of the Korean association of regional geographers
• /
• v.12 no.1
• /
• pp.172-178
• /
• 2006

There has been tremendous increase of disaster related damages since 1990's. Especially flood occurred in summer season highly populated area has led to demolish a lot of facilities and buildings within a short time period. This is to figure out the way to predict the vulnerable flood inundation area by past records of inundation and and geographic information available. The comparative study on 1998 and 1999 flood inundation area in Munsan and Gokneung river shows that 5 degree of slope and 10 m elevation level are dividing index to draw the vulnerable area. This study is to suggest the relatively easy method to predict flood vulnerable area and to apply the results to prepare for protecting the facilities and the people with other thematic geographic database.

• Journal of Wetlands Research
• /
• v.14 no.4
• /
• pp.571-580
• /
• 2012

This study is on the purpose of leading Geomorphoclimatic Instantaneous Unit Hydrograph(GcIUH) by using GIS Techniques, and estimating trigger rainfall for predicting flash flood in Seolmacheon catchment, mountain river watershed. This study leads GcIUH by using GIS techniques, calculates NRCS-CN values for effective rainfall rate, and analyzes 2011 main rainfall events using estimated GcIUH. According to the results, the case of Memorial bridge does not exceed the amount of threshold runoff, however, the case of Sabang bridge shows that simulated peak flow, approximately $149.4m^3/s$, exceeds the threshold runoff. To estimate trigger rainfall, this study determines the depth of 50 year-frequency designed flood amount as a threshold water depth, and estimates trigger rainfall of flash flood in consideration of duration. Hereafter, this study will analyze various flood events, estimate the appropriateness of trigger rainfall as well as threshold runoff through this analysis, and develop prototype of Flash Flood Prediction System which is considered the characteristics of mountain river watershed on the basis of this estimation.

• Journal of Korean Society for Geospatial Information Science
• /
• v.14 no.3 s.37
• /
• pp.87-95
• /
• 2006

Recently, local heavy rain for a short term is caused by unusual changing in the weather. This phenomenon has, several times, caused an extensive flash flood, casualties, and material damage. This study is aimed at calculating the characteristics of flash floods in streams. For this purpose, the analysis of topographical characteristics of water basin through applying GIS techniques will be conducted. The flash flood prediction model we used is made with GCIUH (geomorphoclimatic instantaneous unit hydrograph). The database is established by the use of GIS and by the extraction of streams and watersheds from DEM. The streams studied are included small, middle and large scale watersheds. For the first, for the establishment or criteria on the flash flood warning, peak discharge and trigger runoff must be decided. This study analyzed the degree or aptitude of topographical factors to the trigger runoff calculated by GCUH model.

• Journal of Korea Water Resources Association
• /
• v.35 no.6
• /
• pp.639-650
• /
• 2002

Reservoir operations during the flood season should consider both the flood control and water supply objectives. This study proposed Set Control Algorithm (SCA) as a reservoir operation method, which guarantees both objectives. The concept behind SCA is to provide operators with a set of actions that guarantee feasibility, given a set of operational constraints, and to let them select decisions within a set that satisfies other considerations. The inflow sets used in this study included; observed data, synthetic data, and ESP(Ensemble Streamflow Prediction) scenarios. Applied to the Chungju Dam operations, SCA was compared to the variable flood restricted elevation, as well as the current flood restricted elevation. A 5-year simulation analysis showed that SCA performed better than the other operation methods, and that SCA coupled with ESP performed best among the SCA cases.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.1B
• /
• pp.9-20
• /
• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

• Journal of Advanced Navigation Technology
• /
• v.27 no.1
• /
• pp.133-138
• /
• 2023

As a side effect of recent rapid climate change and global warming, the frequency and scale of flood disasters are increasing worldwide. In Korea, the water level of the Han River is a major management target for preventing flood disasters in Seoul, the capital of Korea. In this paper, to improve the water level prediction of the Han River based on machine learning, we perform a comprehensive assessment of the quality of related dataset and propose data preprocessing methods to improve it. Specifically, we improve the dataset in terms of completeness, validity, and accuracy through missing value processing and cross-correlation analysis. In addition, we conduct a performance evaluation using random forest and LightGBM to analyze the effect of the proposed data improvement method on the water level prediction performance of the Han River.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2002.05a
• /
• pp.43-50
• /
• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.167-167
• /
• 2020

Annual sediment deposition in reservoirs behind weirs poses flood risk, while its accurate prediction remains a challenge. Sediment management by hydraulic flushing is an effective method to maintain reservoir storage. In this study, an integrated approach to predict sediment inflow and sediment flushing simulation in reservoirs is presented. The annual sediment inflow prediction was carried out with Artificial Neural Networks (ANN) modelling. RESCON model was applied for quantification of sediment flushing feasibility criteria. The integrated approach was applied on Sangju Weir and also on estuary of Nakdong River (NREB). The mean annual sediment inflow predicted at Sangju Weir and NREB was 400,000 ㎥ and 170,000 ㎥, respectively. The sediment characteristics gathered were used to setup RESCON model and sediment balance ratio (SBR) and long term capacity ratio (LTCR) were used as flushing efficiency indicators. For Sangju Weir, the flushing discharge, Q_f = 140 ㎥/s with a drawdown of 5 m, and flushing duration, T_f = 10 days was necessary for efficient flushing. At NREB site, the parameters for efficient flushing were Q_f = 80 ㎥/s, T_f = 5 days, N = 1, El_f = 2.24 m. The hydraulic flushing was concluded feasible for sediment management at both Sangju Weir and NREB.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.36 no.3
• /
• pp.395-406
• /
• 2016

Population and development are concentrated by urbanization. Consequently, the usage of underground area and the riverside area have been increased. By increasing impermeable layer, the urban basin drainage is depending on level of sewer. Flood damage is occurred by shortage of sewer capacity and poor interior drainage at river stage. Many of researches about flood stress the unavailability of connection at the river stage with the internal inundation organically. In this study, flood calculated considering rainfall and combined inland-river. Also, using urban runoff model analyze the overflow of sewer. By using results of SWMM model, using flood inundation analysis model analyzed internal drainage efficiency of drainage system. Applying SWMM model, which results to flood inundation analysis model, analyzes internal drainage efficiency of drainage system under localized heavy rain in a basin of the city. The results of SWMM model show the smoothness of internal drainage can be impossible to achieve because of the influence of the river level and sewer overflow appearing. The main manholes were selected as the manhole of a lot of overflow volume. Overflow reduction scenarios were selected for expansion of sewer conduit and instruction retention pond. Overflow volume reduces to 45% and 33~64% by retention pond instruction and sewer conduit expansion. In addition, the results of simulating of flood inundation analysis model show the flood occurrence by road runoff moving along the road slope. Flooded area reduces to 19.6%, 60.5% in sewer conduit expansion scenarios.