• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.295-299
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• 2001

To protect the flooding damages in Medium and Small watershed, it needs to set up flood warning system and develope Flood forecasting Model in real-time basis for medium and small watershed. In this study, it was able to minimize the error range between forecasted flood inflow and actual flood inflow, and forecast accurately the flood discharge some hours in advance by using simplex method recursively for the determination of the best parameters of RETFLO model. The result of RETFLO performance applied to several storm of Yugu river during 3 past years was very good with relative errors of 10% for comparison of total runoff volume and with one hour delayed peak time.

• Journal of Korea Water Resources Association
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• v.39 no.9 s.170
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• pp.755-766
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• 2006

Since the accuracy of climate forecast information has improved from better understanding of the climatic system, particularly, from the better understanding of ENSO and the improvement in meteorological models, the forecasted climate information is becoming the important clue for streamflow prediction. This study investigated the available climate forecast information to improve the extended streamflow prediction in Korea, such as MIMI(Monthly Industrial Meteorological Information) and GDAPS(Global Data Assimilation and Prediction) and measured their accuracies. Both MIMI and the 10-day forecast of GDAPS were superior to a naive forecasts and peformed better for the flood season than for the dry season, thus it was proved that such climate forecasts would be valuable for the flood season. This study then forecasted the monthly inflows to Chungju Dam by using MIMI and GDAPS. For MIMI, we compared three cases: All, Intersection, Union. The accuracies of all three cases are better than the naive forecast and especially, Extended Streamflow Predictions(ESPs) with the Intersection and with Union scenarios were superior to that with the All scenarios for the flood season. For GDAPS, the 10-day ahead streamflow prediction also has the better accuracy for the flood season than for the dry season. Therefore, this study proved that using the climate information such as MIMI and GDAPS to reduce the meteorologic uncertainty can improve the accuracy of the extended streamflow prediction for the flood season.

• Journal of Wetlands Research
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• v.13 no.1
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• pp.13-23
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• 2011

In this study, Mike11 will be used as the numerical model where a data assimilation method will be applied to it. This paper aims to gain an insight and understanding of data assimilation in flood forecasting models. It will start with a general discussion of data assimilation, followed by a description of the methodology and discussion of the statistical error forecast model used, which in this case is the linear regression. This error forecast model is applied to the water level forecast simulated by MIKE11 to produced improved forecast and validated against real measurements. It is found that there exists a phase error in the improved forecasts. Hence, 2 general formula are used to account for this phase error and they have shown improvement to the accuracy of the forecasts, where one improved the immediate forecast of up to 5 hours while the other improved the estimation of the peak discharge.

• 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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• 2008.05a
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• pp.2193-2196
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• 2008

In this study, the new methodology such as support vector machines neural networks model (SVM-NNM) using the statistical learning theory is introduced to forecast flood stage in Nakdong river, Republic of Korea. The SVM-NNM in hydrologic time series forecasting is relatively new, and it is more problematic in comparison with classification. And, the multilayer perceptron neural networks model (MLP-NNM) is introduced as the reference neural networks model to compare the performance of SVM-NNM. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the forecasting of the hydrologic time series in Nakdong river. Furthermore, we can suggest the new methodology to forecast the flood stage and construct the optimal forecasting system in Nakdong river, Republic of Korea.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10d
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• pp.221-224
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• 2007

홍수 예보 시스템은 관측소의 우량계와 수위계로부터 데이터를 수집하여, VHF, SATELLITE 등의 통신망을 이용하여 통제소의 TM 주장비로 수집한 데이터를 송신한다. 그리고 통제소에서는 수신된 데이터를 분석하여 홍수예보에 활용하고 있다. 그러나 현 시스템은 제한적인 무선 시스템으로 관측소의 자료 확인이나 테스트를 위해서는 직접적인 수동접속 또는 엔지니어의 대국이 불가피하다. 본 연구에서는 기존의 홍수예보 시스템에 CDMA라 Bluetooth 기능을 적용하여 지능적 홍수예보시스템(IFFS, Intelligence Flood Forecast System)의 개발하였다. 지능적 통수예보시스템의 도입 시 항시 휴대 가능한 PDA를 이용, 관측소의 수위, 우량데이터를 Bluetooth를 이용하여 백업하며 수집된 데이터를 통제소로 간편하게 전승할 수 있으며, 관측소의 S/N 측정을 인력과 시간의 낭비 없이 효율적으로 수행할 수 있다.

• Journal of Industrial Technology
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• v.18
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• pp.51-59
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• 1998

To flood forecastion, until now, Storage function method, Streamflow Synthesis and Reservoir Regulation, and HEC-1 model have been analysed generally in various definite simulation. Generally, Streamflow Synthesis and Reservoir Regulation and HEC-1 model are more delicacy and more excellent model than Storage function method in physically. But the resource huge for test of models. On the contrary, Storage function method has not only a few model various and data for decision but also has poor theory background in model excessively simpled water circulation about a basin. In this reason, this study is purpose to develop a statistical flood forecasting model that can forecast with accuracy variety of water height to Nak-Dong river vibration spots in flood with accumulated water resource.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.362-362
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• 2020

Flash flood is a dangerous weather phenomenon, affecting humans and the economy. The identification, forecast of the changing trend and its characteristics are increasingly concerned. In the world, there have many methods for determining the characteristics of flash floods, in which flash flood guidance (FFG) is a fast, effective and widely used method. The main source of flash floods is short-term rainfall. In this study, we used the data of cross-sectional measurement at the tributaries and the hourly rain data from the automatic rainfall measurement stations in the Geum river basin. Besides, we use a combination of the flash flood guidance and the best fit distribution function to estimate the repeatability of flash floods for head-water catchments in Geum river basin. In which, FFG determines the threshold of rainfall for flash floods. The study has determined the best hourly rainfall distribution function for the Geum river basin and estimated the maximum rainfall of 1hr according to the return periods.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.382-382
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• 2006

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.383-383
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• 2006