• Proceedings of the Korea Water Resources Association Conference
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• 2000.05a
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• pp.333-340
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• 2000

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.1023-1034
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• 2008

The size and frequency of the natural disaster related to the severe storms are increased for recent decades in all over the globe. The damage from natural disasters such as typhoon, storm and local severe rainfall is very serious in Korea since they are concentrated on summer season. These phenomena will be more frequent in the future because of the impact of climate change related to increment of $CO_2$ concentration and the global warming. To reduce the damage from severe storms, a short-range precipitation forecasting model using a weather radar was developed. The study was conducted as following four tasks: conversion three-dimensional radar data to two-dimensional CAPPI(Constant Altitude Plan Position Indicator) efficiently, prediction of motion direction and velocity of a weather system, estimation of two-dimensional rainfall using operational calibration. Results demonstrated that two-dimensional estimation using weather radar is useful to analyze the spatial characteristics of local storms. If the precipitation forecasting system is linked to the flood prediction system, it should contribute the flood management and the mitigation of flood damages.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.515-526
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• 2021

The frequency of typhoons and torrential rainfalls has increased due to climate change, and the concurrent risk of breakage of dams and reservoirs has increased due to structural aging. To cope with the risk of dam breakage, a more accurate emergency action plan (EAP) must be established, and more advanced technology must be developed for the prediction of flooding. Hence, the present study proposes a method for establishing a more effective EAP by performing flood and inundation analyses using one- and two-dimensional models. The probable maximum flood (PMF) under the condition of probable maximum precipitation (PMP) was calculated for the target area, namely the Gyeong-cheon reservoir watershed. The breakage scenario of the Gyeong-cheon reservoir was then built up, and breakage simulations were conducted using the dam-break flood forecasting (DAMBRK) model. The results of the outflow analysis at the main locations were used as the basis for the one-dimensional (1D) and two-dimensional (2D) flood inundation analyses using the watershed modeling system (WMS) and the FLUvial Modeling ENgine (FLUMEN), respectively. The maximum inundation area between the Daehari-cheon confluence and the Naeseong-cheon location was compared for each model. The 1D flood inundation analysis gave an area of 21.3 km², and the 2D flood inundation analysis gave an area of 21.9 km². Although these results indicate an insignificant difference of 0.6 km² in the inundation area between the two models, it should be noted that one of the main locations (namely, the Yonggung-myeon Administrative and Welfare Center) was not inundated in the 1D (WMS) model but inundated in the 2D (FLUMEN) model.

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

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.341-351
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• 2008

Since the damage from the torrential rain increases recently due to climate change and global warming, the significance of flood forecasting and warning becomes important in medium and small streams as well as large river. Through the preprocess and main processes for estimating runoff, diverse errors occur and are accumulated, so that the outcome contains the errors in the existing flood forecasting and warning method. And estimating the parameters needed for runoff models requires a lot of data and the processes contain various uncertainty. In order to overcome the difficulties of the existing flood forecasting and warning system and the uncertainty problem, ANFIS(Adaptive Neuro-Fuzzy Inference System) technique has been presented in this study. ANFIS, a data driven model using the fuzzy inference theory with neural network, can forecast stream level only by using the precipitation and stream level data in catchment without using a lot of physical data that are necessary in existing physical model. Time series data for precipitation and stream level are used as input, and stream levels for t+1, t+2, and t+3 are forecasted with this model. The applicability and the appropriateness of the model is examined by actual rainfall and stream level data from 2003 to 2005 in the Tancheon catchment area. The results of applying ANFIS to the Tancheon catchment area for the actual data show that the stream level can be simulated without large error.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.694-698
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• 2005

본 연구에서는 우리나라 5대강 유역에 대한 홍수예경보시스템의 홍수추적방법으로 이용되고 있는 물리적인 모형인 저류함수법의 한계점을 극복하고, 영산강 유역의 본류를 대표하는 나주지점과 황룡강 유역을 대표하는 선암지점에 대하여 유역의 수문학적 구조를 나타내지 않는 인공신경망 이론을 이용하여 강우-유출 과정의 비선형 모형을 개발하였다. 또한, 신속한 홍수유출량 예측과 예측 결과에 따른 현장 적용이 가능하도록 CS(Client-Server) 기반에서 인공신경망에 대한 원시코드(source code)를 GUI(Graphical User Interface)화하여 홍수예측시스템(Flood Forecasting System : FFS)을 개발하였다. 본 연구결과 나주지점에서는 Model II의 ANN_NJ_9 모형이 선암지점에서는 Model III의 ANN_SA_9 모형이 강우-유출 특성을 가장 잘 반영하였다. 또한, 본 연구에서 개발한 GUI_FFS에 대하여 기 확보된 2004년도 강우 및 유출량 적용한 결과 0.98이상의 $R^2$값을 보임으로서 향후 수자원 및 하천계획 수립과 그에 따른 운영 및 관리에 효율성을 더할 수 있을 것이라 판단된다.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.23-31
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• 2007

The objective of the study is to prepare input data for FIA (Flood Inundation Analysis) & FDA (Flood Damage Assessment) through rainfall-runoff simulation by HEC-HMS model. For HwaOng watershed (235.6 $km^{2}$), HEC-HMS was calibrated using 6 storm events. Geospatial data processors, HEC-GeoHMS is used for HEC-HMS basin input data. The parameters of rainfall loss rate and unit hydrograph are optimized from the observed data. HEC-HMS was applied to simulate rainfall-runoff relation to frequency storm at the HwaOng watershed. The results will be used for mitigating and predicting the flood damage after river routing and inundation propagation analysis through various flood scenarios.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.2
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• pp.27-35
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• 2006

Large dams are managed with operation standard and flood forecasting systems, while small dams do not have management method generally. Shortage of water resources and natural disasters due to drought and flood raised public concerns for management of small dams. Most of small dams are irrigation dams, which need diversified water uses. However, the lack of systematic management of small dams have caused serious water wastage and increased natural disasters. Storage management method and system were developed to solve these problems in small dams. The system was applied to Seongju dam for effective management. The storage management method was established considering hydrology simulation and statistical analysis using the system. This method can bring additional available water, even in the same conditions of the water demand and the supply conditions of watershed. It can improve the flood control capacity and water utilization efficiency by' the flexible operation of storage space.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.347-359
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• 2010

A simple web-based flood forecasting system using data from stage and rainfall monitoring stations was developed to solve the difficulty that real-time forecasting model could not get the reliabilities because of assumption of future rainfall duration and intensity. The regression model in this research could forecast future water level of maximum 2 hours after using data from stage and rainfall monitoring stations in Daejeon area. Real time stage and rainfall data were transformed from web-sites of Geum River Flood Control Office & Han River Flood Control Office based MS-Excel 2007. It showed stable forecasts by its maximum standard deviation of 5 cm, means of 1~4 cm and most of improved coefficient of determinations were over 0.95. It showed also more researches about the stationarity of watershed and time-series approach are necessary.