• Journal of Wetlands Research
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• v.12 no.3
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• pp.155-163
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• 2010

The flash flood is characterized as flood leading to damage by heavy rainfall occurred in steep slope and impervious area with short duration. Flash flood occurs when rainfall exceeds Flash Flood Guidance(FFG). So, the accurate estimation of FFG will be helpful in flash flood forecasting and warning system. Say, if we can reduce the uncertainty of rainfall-runoff relationship, FFG can be estimated more accurately. However, since the rainfall-runoff models have their own parameter characteristics, the uncertainty of FFG will depend upon the selection of rainfall-runoff model. This study used four rainfall-runoff models of HEC-HMS model, Storage Function model, SSARR model and TANK model for the estimation of models' uncertainties by using Monte Carlo simulation. Then, we derived the confidence limits of rainfall-runoff relationship by four models on 95%-confidence level.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.449-453
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• 2007

Has been being operated since 1974, recently, the flood forecasting and warning system is applied in almost all the rivers in Korea, and the Storage Function Method(SFM) is used for flood routing. The SFM which was presented by Toshimitsu Kimura(1961) routes floods in channels and basins with the storage function as the basic equation. A watershed is devided into two zone, runoff and percolation area and Runoff is occured when cumulated rainfall is not exceed saturation rainfall, but exceed, runoff is occured from percolation area, too. Runoff area is given and not changed, runoff ratio is constant. In routing process, runoff from runoff and percolation area is routed seperately with nonlinear cenceptual reservior having same characteristics and it is unreasonable assumption. Modified SFM is proposed with storage function and continuity Equation which has no assumption for routing process and effective rainfall is calculated by SCS Method. For Wi Stream, comparision of Kimura and Modified SFM is conducted and It could be seen that Modified SFM is more improvemental and easily applicable method.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.191-201
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• 2018

Recently, the frequency of heavy rainfall is increasing due to the effects of climate change, and heavy rainfall in urban areas has an unexpected and local characteristic. Floods caused by localized heavy rains in urban areas occur rapidly and frequently, so that life and property damage is also increasing. It is crucial how fast and precise observations can be made on successful flood management in urban areas. Local heavy rainfall is predominant in low-level storms, and the present large-scale radars are vulnerable to low-level rainfall detection and observations. Therefore, it is necessary to introduce a new urban flood forecasting system to minimize urban flood damage by upgrading the urban flood response system and improving observation and forecasting accuracy by quickly observing and predicting the local storm in urban areas. Currently, the WHAP (Water Hazard Information Platform) Project is promoting the goal of securing new concept water disaster response technology by linking high resolution hydrological information with rainfall prediction and urban flood model. In the WHAP Project, local rainfall detection and prediction, urban flood prediction and operation technology are being developed based on high-resolution small radar for observing the local rainfall. This study is expected to provide more accurate and detailed urban flood warning system by enabling high-resolution observation of urban areas.

• Journal of Korea Water Resources Association
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• v.39 no.12 s.173
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• pp.1013-1022
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• 2006

Urban flooding is usually caused by the surcharge of storm sewers. For this reason, previous studies on urban flooding are mainly concentrated on the simulation of urban drainage systems. However these approaches that find the pipes which have insufficient drainage capacity are very approximate and unreasonable ways in establishing both flood prevention and flood-loss reduction planning. In this study, a two-dimensional model linked the existing ILLUDAS model is developed to calculate the accurate and resonable solution about urban flood inundation and it is verified by using the simulation of July 2001 flood in Seoul. In the urban area with a small difference of ground elevations, the two-dimensional flood propagation phases must be considered to make a accurate analysis for inundated area and depth. The result of this study can be used to construct fundamental data for a flood control plan and establish a urban flood forecasting/warning system.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.221-231
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• 2010

A regression water level forecasting model using data from stage and rainfall monitoring stations is developed to solve the difficulties which real-time forecasting models could not get the reliabilities by assuming future rainfall duration and intensity. The model could forecast future water levels of maximum 2 hours after using data from monitoring stations in Daejeon area. It shows stable forecasts by its maximum standard deviation is 5 cm, average standard deviations are 1~4 cm and most of coefficients of determination are larger than 0.95. It shows also more researches about the stationary of watershed which assumed in this regression method are necessary.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.294-294
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• 2019

The system for predicting flood of river at Flood Control Office is made up of a rainfall-runoff model and FLDWAV model. This system is mainly operating to predict the excess of the flood watch or warning level at flood forecast points. As the demand for information of the management and operation of riverside, which is being used as a waterfront area such as parks, camping sites, and bike paths, high-level forecasts of watch and warning at certain points are required as well as production of lowland flood forecast information that is used as a waterfront within the river. In this study, a technology to produce flood forecast information in lowland areas of the river used as a waterfront was developed. Based on the results of the 1D hydraulic analysis, a model for performing spatial operations based on high resolution grid was constructed. A model was constructed for Andong district, and the inundation conditions and level were analyzed through a virtual outflow scenarios of Andong and Imha Dam.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Electronics and Telecommunications Trends
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• v.35 no.1
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• pp.80-88
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• 2020

This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

• Atmosphere
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• v.27 no.2
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• pp.189-197
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• 2017

Progressing from weather forecasts and warnings to multi-hazard impact-based forecast and warning services represents a paradigm shift in service delivery. Urban flooding is a typical meteorological disaster. This study proposes support plan for urban flooding impact-based forecast by providing inundation risk matrix. To achieve this goal, we first configured storm sewer management model (SWMM) to analyze 1D pipe networks and then grid based inundation analysis model (GIAM) to analyze 2D inundation depth over the Gangnam drainage area with $7.4km^2$. The accuracy of the simulated inundation results for heavy rainfall in 2010 and 2011 are 0.61 and 0.57 in POD index, respectively. 20 inundation scenarios responding on rainfall scenarios with 10~200 mm interval are produced for 60 and 120 minutes of rainfall duration. When the inundation damage thresholds are defined as pre-occurrence stage, occurrence stage to $0.01km^2$, 0.01 to $0.1km^2$, and $0.1km^2$ or more in area with a depth of 0.5 m or more, rainfall thresholds responding on each inundation damage threshold results in: 0 to 20 mm, 20 to 50 mm, 50 to 80 mm, and 80 mm or more in the rainfall duration 60 minutes and 0 to 30 mm, 30 to 70 mm, 70 to 110 mm, and 110 mm or more in the rainfall duration 120 minutes. Rainfall thresholds as a trigger of urban inundation damage can be used to form an inundation risk matrix. It is expected to be used for urban flood impact forecasting.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.12-31
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• 2010

The objective of this study is to evaluate the applicability of GIS based multi-directional flow allocation model. In order to evaluate the suggested model in this study, it was applied to real watersheds, Pyeongchang and Soyang river basin. The simulation results were compared with observed values, and showed good agreements. The improvement of accuracy and reduction of simulation time were carried out by applying multi-directional flow allocation. Accordingly, the applied methodologies presented in this study will be used to predict accurate runoff, which plays a major role in integrated flood management. If this model is combined with the techniques of rainfall forecasting, it will contribute to the real-time flood forecasting and warning in the future.