• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.30-37
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• 2023

This study aimed to evaluate the ability to reduce flood damage caused by abnormal rainfall events due to climate change by utilizing a blue-green roof (BGF), a type of rooftop greening technology. For two buildings with the same roof area, a BGF was installed in the experimental group, a general roof was configured in the control group, and rainfall runoff was compared. A total of 10 rainfall events were tested and analyzed by classifying them into three rainfall classes (less than 10 mm, less than 100 mm, and more than 100 mm). There was a reduction of 100% in the case of 10 mm or less of rainfall, 84. 7% in the case of 100 mm or less, and 39.8% in the case of 100 mm or more. Although this study showed that a BGF was effective in reducing rainfall runoff, additional experiments and analyses of various factors affecting rainfall runoff reduction are needed to generalize the results of the study. This research methodology may be used to develop a method for evaluating the resilience of a BGF to flood damage due to climate change.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.519-528
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• 2006

In the construction of hydraulic structures deciding a design flood is one of the most important works. It should be especially noted that the time distribution of the design rainfall method makes a significant effect on the results of the design flood. Thus, choosing an appropriate time distribution method for the design rainfall is a very important process. In recent years, Huff's method is usually used in Korea. This method presents dimensionless rainfall-time cumulative curves, which are made through the analyses of storm data. In this study, the annual maximum rainfall data, from 1961 to 2004 were analyzed to make the dimensionless rainfall-time cumulative curves and hyetographs in Seoul. The results were compared with the "Regional Time Distribution of the Design Rainfall", (KICT, 1989 and MCT, 2000). As a result, the dimensionless rainfall-time cumulative curves are smoother than Huff's results when the duration of an annual maximum rainfall is short. In addition, the curves are similar with the Huff's results as the duration is longer.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1172-1175
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• 2006

Generally, the estimation of design flood uses basin rainfall data, water level data, and runoff data, and so forms rainfall-runoff model. Because owing to the lack of hydrological data, the decision of representative unit hydrograph about the basin is difficult, the estimation of design flood uses topography feature data, and so presumes variables, and then applies the presumed variables to the model. In estimating design flood by using the model, it is considerably difficult to analyze how the model input variables estimated by topography factors, or the design flood data estimated previously are related to basin feature factors as the basic data, and presume design flood in the unmeasured basins or the basins where river arrangement basic plan is not established. The purpose of this study is to analyze how the design flood estimated previously by river arrangement basic plan is correlated with topography factors in presuming design flood, and so examine the presumption measures of design flood by using topography feature data and probability rainfall data.

• 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.54 no.12
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• pp.1329-1338
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• 2021

Rain radar provides high spatio-temporal radar rainfall that can be used as input data to short-term precipitation forecasting models. Korea Institute of Civil Engineering and Building Technology (KICT) has developed a flash flood forecasting system that is providing flash flood forecasting based on short-term rainfall forecasts estimated by the radar rainfall. Accuracy of the radar rainfall as well as the short-term rainfall forecasts, however, can deteriorate when radar polarimetric variables have error. In this study, we develope real-time program that can correct the error inherent in the radar polarimetric variables. First, effect according to the correction of the error was verified using 363 rainfall events on non real-time. The accuracy (1-NE) of the radar rainfall was approximately 70% and correlation coefficient was higher than 0.8 after correcting the error on non real-time. The accuracy (1-NE) using the real-time program was also approximately 70% after correcting the error.

• Journal of Korea Water Resources Association
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• v.48 no.4
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• pp.257-268
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• 2015

The objective of this study is to assess the dual-polarization radar for flood forecasting. First, radar rainfall has temporal and spatial errors, so estimated radar rainfall was compared with ground observation rainfall to assess accuracy improvement, especially, considering the radar range of observation and increase of the rainfall intensity. The results of this study showed that the error for estimated dual-polarization radar rainfall was less than single-polarization radar rainfall. And in this study, dual-polarization radar rainfall for flood forecasting was assessed using MAP (Mean Areal Precipitation) and SURR (Sejong University Rainfall Runoff) model in Namkang dam watershed. The results of MAP are more accurate using dual-polarization radar. And the results of runoff using dual-polarization radar rainfall showed that peak flow error was reduced approximately 12~63%, runoff volumes error was reduced by approximately 30~42%, and also the root mean square error decreased compared to the result of runoff using single-polarization radar rainfall. The results revealed that dual-polarization radar will contribute to improving the accuracy of the flood forecasting.

• Economic and Environmental Geology
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• v.56 no.1
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• Journal of Environmental Science International
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• v.10 no.3
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• pp.217-223
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• 2001

Where no records are available at a site, a preliminary estimate may be made from relations between floods and catchment chatacteristics. A number of these chatacteristics were chosen for testing and were measured for those catchments where mean annual flood estimates were available. Although the improvement using extended data in regression of flood estimates on catchment characteristics was small, this may be due to the limitations of the regression model. When an individual short term record is to be extended, more detailed attention can be given; an example is presented of the technique which should be adopted in practice, particularly when a short term record covers a period which is known to be biassed. A method of extending the peaks over a threshold series is presented with a numerical example. The extension of records directly from rainfall by means of a conceptual model is discussed, although the application of such methods is likely to be limited by lack of recording raingauge information. Methods of combining information from various sources are discussed in terms of information from catchment characteristics supplemented by records. but are generally applicable to different sources of information. The application of this technique to estimating the probable maximum flood requires more conservative assumptions about the antecedent condition, storm profile and unit hydrograph. It is suggested that the profile and catchment wetness index at the start of the design duration should be based on the assumption that the estimated maximum rainfall occurs in all durations centered on the storm peak.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Journal of Korea Water Resources Association
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• v.40 no.6 s.179
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• pp.447-458
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• 2007

In this study, the coastal urban flood prediction and warning system based on HEC-RAS and SWMM were investigated to evaluate a watershed of On-Cheon stream in Busan which has characteristics of costal area cased by flooding of coastal urban areas. The basis of this study is a selection of various geological data from the numerical map that is a watershed of On-Cheon stream and computation of hydrologic GIS data. Thiessen method was used for analyzing of rainfall on the On-Cheon stream and 6th regression equation, which is Huff's Type II was time-distribution of rainfall. To evaluate the deployment of flood prediction and warning system, risk depth was used on the 3 selected areas. To find the threshold runoff for hydraulic analysis of stream, HEC-RAS was used and flood depth and threshold runoff was considered with the effect of tidal water level. To estimate urban flash flood trigger rainfall, PCSWMM 2002 was introduced for hydrologic analysis. Consequently, not only were the criteria of coastal urban flood prediction and warning system decided on the watershed of On-Cheon stream, but also the deployment flow charts of flood prediction and warning system and operation system was evaluated. This study indicates the criteria of flood prediction and warning system on the coastal areas and modeling methods with application of ArcView GIS, HEC-RAS and SWMM on the basin. For the future, flood prediction and warning system should be considered and developed to various basin cases to reduce natural flood disasters in coastal urban area.