• Journal of Korea Water Resources Association
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• v.46 no.1
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• pp.47-58
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• 2013

Recently, an increase in the occurrence of sudden local flooding of great volume and short duration due to global climate changes has occasioned the significant danger and loss of life and property in Korea as well as most parts of the world. Such a local flood that usually occurs as the result of intense rainfall over small regions rises quite quickly with little or no advance warning time to prevent flood damage. To prevent the local flood damage, it is important to quickly predict the flood severity for flood events exceeding a threshold discharge that may cause the flood damage for inland areas. The aim of this study is to develop the NFI (New Flood Index) measuring the severity of floods in small ungauged catchments for use in local flood predictions by the regression analysis between the NFI and rainfall patterns. Flood runoff hydrographs are generated from a rainfall-runoff model using the annual maximum rainfall series of long-term observations for the two study catchments. The flood events above a threshold assumed as the 2-year return period discharge are targeted to estimate the NFI obtained by the geometric mean of the three relative severity factors, such as the flood magnitude ratio, the rising curve gradient, and the flooding duration time. The regression results show that the 3-hour maximum rainfall depths have the highest relationships with the NFI. It is expected that the best-fit regression equation between the NFI and rainfall characteristics can provide the basic database of the preliminary information for predicting the local flood severity in small ungauged catchments.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.637-640
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• 2008

Flood control and river improvement works are carried out every year for the defense of the flood disaster, it is impossible to avoid the damage when there is a flood exceeding the capacity of hydraulic structures. Therefore, nonstructural counter plans such as the establishment of flood hazard maps, the flood warning systems are essential with structural counter plans. In this study, analysis of the internal inundation effect using rainfall runoff model such as PC-SWMM was applied to Woo Ee experimental stream basin. Also, the design frequency analysis for effects of the external inundation was accomplished by main parameter estimation for conclusive hydraulic routing using HEC-RAS model. Finally, inundated areas for flood hazard map were estimated at Woo Ee downstream basin according to flood frequency using HEC-GeoRAS model linked by Arc View GIS.

• Water for future
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• v.18 no.2
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• pp.153-161
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• 1985

The methods for hydrologic prediction of streamflows for more efficient and functional operations and automation of the flood warning and forecasting system have been studiedand which have been widely used in the control engineering have been studied and investigated for representation of the dynamic behavior of rainfall-runoff precesses, and formulated into mathematical model form. The applicabilities of the model using the adaptive Kalman filter algorithm to the on-line, real-time prediction of river flows have been worked out. The computer programs in FORTRAN which are developed here can be utilized for more efficient operations and better prediction abilities of flood warning and forecasting systems, and also should be modified for better model performance.

• 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.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2004.11a
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• pp.87-87
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• 2004

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.530-535
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• 2005

To monitor the flood level under heavy rainfall and the scour at railroad bridge, the system, which can effectively collect, store and transmit the data, is developed and applied to the field. The results in this study are as follows. 1) Monitoring for water level and scour depth is well done in view of the recording velocity and the accuracy of data which are measured. 2) This system is based on the web, internet and it is able to collect the realtime data and to analyze the risk. 3) When water level excesses the limit of danger level of a river on which railroad bridge is located, or when scour depth and angle of inclination of pier is increased, the scenario for early warning signal which sends to managers at central traffic control and drivers of trains is automatically made. It is judged that this system secures the safety of railroad and protects lives of train passengers as the warning signal sends to running train in advance at risky situation of railroad bridge under heavy rainfall.

• 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.

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.317-327
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• 2005

The flood is the most general and frequently occurs among natural disasters. Generally flood by the rainfall which extends superexcellently for the occurrence but flash flood from severe rain storm gets up an absurd drowsiness at grade hour. This paper aims to 1 hour real-time flash flood and predict possibility at the area where is the possible flood will occur from the rainfall hour mountain after acquiring data in GIS(Geographic Information System) base by GcIUH(Geomorphoclimatic Instantaneous Unit Hydrograph). The flash flood occurrence is set up at 0.5m, 0.7m and 1.0m in standard depth. And this study suggests standard flood alarm which designed by probable flood according to duration time. The research result shows real-time flash flood evaluation has the suitable standard in the basin when comparing with the existing official warning announcement system considering topographical information.

• Water for future
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• v.29 no.6
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• pp.237-247
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• 1996

In this study two rainfall-runoff models, the NWS-PC model and the Storage Function Model (SFM), were compared to see their applicability in the flood forecasting at the river system. The SFM has been adopted in the flood-forecasting and warning system for the major rivers in Korea since 1974, and the NWS-PC model, a physically based model, has been developed to simulate soil moisture changing as well as the surface and subsurface flow at the watershed and in the river streams. Case studies were carried out using flood event data observed at the Mihochun watershed in Geum-river basin during 1985 to 1995. Simulated results from both models were compared with the observed data with respect to the RMS errors and relative errors for peak flow discharges and total runoff volumes to show the advantages and disadvantages of both models and to suggest the way to improve their performances.

• 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.