• Journal of Korea Water Resources Association
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• v.37 no.9
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• pp.771-780
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• 2004

The purpose of this study is to test a development of system for flood runoff forecasting using neural network model. As the forecasting models for flood runoff the neural network model was tested with the observed flood data at Gongju and Buyeo stations. The neural network model consists of input layer, hidden layer, and output layer. For the flood events tested rainfall and runoff data were the input to the input layer and the flood runoff data were used in the output layer. To make a choice the forecasting model which would make up of runoff forecasting system properly, real-time runoff of river when flood periods were forecasted by using neural network model and state-space model. A comparison of the results obtained by the two forecasting models indicated the superiority and reliability of the neural network model over the state-space model. The neural network model was modified to work in the Web and developed to be the basic model of the forecasting system for the flood runoff. The neural network model developed to be used in the Web was loaded into the server and was applied to the main stream of Geum river. For the main stage gauging stations mentioned above the applicability of the selected forecasting model, the Neural Network Model, was verified in the Web.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.512-518
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• 1999

Recently rainfall and water evel are monitored via on -line system in real-time bases. We applied the on-line system to get the rainfall and waterlevel data for the development of the real-time flood forecasting model based on SCS method in hourly bases. Main parameters for the model calibration are concentration time of flood and soil moisture condition in the watershed. Other parameters of the model are based on SCS TR-%% and DAWAST model. Simplex method is used for promoting the accuracy of parameter estimation. The basic concept of the model is minimizing the error range between forcasted flood inflow and actual flood inflow, and accurately forecasting the flood discharge some hours in advance depending on the concentration time. The flood forecasting model developed was applied to the Yedang and Topjung reservoir.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.405-408
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• 2008

Up to now, a lot of houses, roads and other urban facilities have been damaged by natural disasters such as flash floods and landslides. It is reported that the size and frequency of disasters are growing greatly due to global warming. In order to mitigate such disaster, flood forecasting and alerting systems have been developed for the Han river, Geum river, Nak-dong river and Young-san river. These systems, however, do not help small municipal departments cope with the threat of flood. In this study, a real-time urban flood forecasting service (U-FFS) is developed for ubiquitous computing city which includes small river basins. A test bed is deployed at Tan-cheon in Gyeonggido to verify U-FFS. It is found that U-FFS can forecast the water level of outlet of river basin and provide real-time data through internet during heavy rain. Furthermore, it is expected that U-FFS presented in this study can be applied to ubiquitous computing city (u-City) and/or other cities which have suffered from flood damage for a long time.

• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.67-75
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• 2004

In this study, real-time forecasting model(Neural Dam Inflow Forecasting Model; NDIFM) based on neural network to predict the dam inflow which is occurred by flood runoff is developed and applied to check its availability for the operation of multi-purpose reservoir Developed model Is applied to predict the flood Inflow on dam Nam-Gang in Nak-dong river basin where the rate of flood control dependent on reservoir operation is high. The input data for this model are average rainfall data composed of mean areal rainfall of upstream basin from dam location, observed inflow data, and predicted inflow data. As a result of the simulation for flood inflow forecasting, it is found that NDIFM-I is the best predictive model for real-time operation. In addition, the results of forecasting used on NDIFM-II and NDIFM-III are not bad and these models showed wide range of applicability for real-time forecasting. Consequently, if the quality of observed hydrological data is improved, it is expected that the neural network model which is black-box model can be utilized for real-time flood forecasting rather than conceptual models of which physical parameter is complex.

• Water Engineering Research
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• v.3 no.2
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• pp.123-134
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• 2002

A developed Quantitative Flood Forecasting (QFF) model was applied to the mid-Atlantic region of the United States. The model incorporated the evolving structure and frequency of intense weather systems of the study area 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 associated with synoptic atmospheric conditions as Input. Here, we present results from the application of the Quantitative Flood Forecasting (QFF) model in 2 small watersheds along the leeward side of the Appalachian Mountains in the mid-Atlantic region. Threat scores consistently above 0.6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 40% and up to 55 % were obtained.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1809-1827
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• 2013

Recently we need to take various measures to prepare for extreme flood that occur due to climate change. It is important that establish flood forecasting system to prepare flood over non-structure measures. The objective of this study is to develop superior real-time flood forecasting model by comparing the Neuro-fuzzy model and the multiple linear regression model. The Neuro-fuzzy model and the multiple linear regression model are established using same input data and applied for various flood events in Nakdong basin. The results show that the Neuro-fuzzy model can carry out flood forecasting results more accurately than the multiple linear regression model. This study can contribute to the establishment of a high accuracy flood information system that secure lead time in Nakdong basin.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.390-396
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• 2000

The basic concept of the model is minimizing the error range between forecasted flood inflow and actual flood inflow, and accurately forecasting the flood discharge some hours in advance depending on the concentration time(Tc) and soil moisture retention storage(Sa). Simplex method that is a multi-level optimization technique was used to search for the determination of the best parameters of RETFLO (REal-Time FLOod forecasting)model. The flood forecasting model developed was applied to several strom events of Yedang reservoir during past 10 years. Model perfomance was very good with relative errors of 10% for comparison of total runoff volume and with one hour delayed peak time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.118-123
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• 2015

There have been various studies on measurements of flood risk and forecasting models. For river and dam region, PDF and FVI has been proposed for measurement of flood risk and regression models have been applied for forecasting model. For Bo region unlikely river or dam region, flood risk would unexpectedly increase due to outgoing water to keep water amount under the designated risk level even the drain system could hardly manage the water amount. GFI and general linear model was proposed for flood risk measurement and forecasting model. In this paper, FVI with the consideration of duration on GFI was proposed for flood risk measurement at Bo region. General linear model was applied to the empirical data from Bo region of Nadong river to derive the forecasting model of FVI at three different values of Base High Level, 2m, 2.5m and 3m. The significant predictor variables on the target variable, FVI were as follows: ground water level based on sea level with negative effect, difference between ground altitude of ground water and river level with negative effect, and difference between ground water level and river level after Bo water being filled with positive sign for quantitative variables. And for qualitative variable, effective soil depth and ground soil type were significant for FVI.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.181-184
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• 2007

Up to now, a lot of houses, roads and other urban facilities have been damaged by natural disasters such as flash floods and landslides. It is reported that the size and frequency of disasters are growing greatly due to global warming. In order to mitigate such disaster, flood forecasting and alerting systems have been developed for the Han river, Geum river, Nak-dong river and Young-san river. These systems, however, do not help small municipal departments cope with the threat of flood. In this study, a real-time urban flood forecasting service (U-FFS) is developed for ubiquitous computing city which includes small river basins. A test bed is deployed at Tan-cheon in Gyeonggido to verify U-FFS. Wireless sensors such as rainfall gauge and water lever gauge are installed to develop hydrologic forecasting model and CCTV camera systems are also incorporated to capture high definition images of river basins. U-FFS is based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) that is data-driven model and is characterized by its accuracy and adaptability. It is found that U-FFS can forecast the water level of outlet of river basin and provide real-time data through internet during heavy rain. It is revealed that U-FFS can predict the water level of 30 minutes and 1 hour later very accurately. Unlike other hydrologic forecasting model, this newly developed U-FFS has advantages such as its applicability and feasibility. Furthermore, it is expected that U-FFS presented in this study can be applied to ubiquitous computing city (U-City) and/or other cities which have suffered from flood damage for a long time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.