• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.381-393
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• 2021

As part of the National Disaster Management Research Institute's Official Development Assistance (ODA) projects for transferring new technologies in the field of disaster-safety management, a flood forecasting and warning system was established in 2019 targeting the Borikhan in the Namxan River Basin in Bolikhamxai Province, Laos. In the target area, which is an ungauged small and medium river basin, observation stations for real-time monitoring of rainfall and runoff and alarm stations were installed, and a software that performs real-time data management and flood forecasting and warning functions was also developed. In order to establish a flood warning standard and develop a nomograph for flood prediction, hydraulic and hydrological analysis was performed based on the 30-year annual maximum daily rainfall data and river morphology survey results in the target area. This paper introduces the process and methodology used in this study, and presents the results of the system's applicability review based on the data observed and collected in 2020 after system installation.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1107-1119
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• 2012

The objectives of this study are to develop the flow nomograph for real-time flood forecasting and to assess its applicability in restored Cheonggye stream. The Cheonggye stream basin has the high impermeability and short concentration time and complicated hydrological characteristics. Therefore, the flood prediction method using runoff model is ineffective due to the limit of forecast. Flow nomograph which is able to forecast flood only with rainfall information. To set the forecast criteria of flow nomograph at selected flood forecast points and calculated criterion flood water level for each point, and in order to reflect various flood events set up simulated rainfall scenario and calculated rainfall intensity and rainfall duration time for each condition of rainfall. Besides, using a rating curve, determined scope of flood discharge following criterion flood water level and using SWMM model calculated flood discharge for each forecasting point. Using rainfall information following rainfall scenario calculated above and flood discharge following criterion flood water level developed flow nomograph and evaluated it by applying it to real flood event. As a result of performing this study, the applicability of flow nomograph to the basin of Cheonggye stream appeared to be high. In the future, it is reckoned to have high applicability as a method of prediction of flood of urban stream basin like Cheonggye stream.

• 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 The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.11-25
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• 2021

The objective of this study was to analyze the impact of activation functions on flood forecasting model based on Artificial neural networks (ANNs). The traditional activation functions, the sigmoid and tanh functions, were compared with the functions which have been recently recommended for deep neural networks; the ReLU, leaky ReLU, and ELU functions. The flood forecasting model based on ANNs was designed to predict real-time runoff for 1 to 6-h lead time using the rainfall and runoff data of the past nine hours. The statistical measures such as R2, Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), the error of peak time (ETp), and the error of peak discharge (EQp) were used to evaluate the model accuracy. The tanh and ELU functions were most accurate with R2=0.97 and RMSE=30.1 (㎥/s) for 1-h lead time and R2=0.56 and RMSE=124.6~124.8 (㎥/s) for 6-h lead time. We also evaluated the learning speed by using the number of epochs that minimizes errors. The sigmoid function had the slowest learning speed due to the 'vanishing gradient problem' and the limited direction of weight update. The learning speed of the ELU function was 1.2 times faster than the tanh function. As a result, the ELU function most effectively improved the accuracy and speed of the ANNs model, so it was determined to be the best activation function for ANNs-based flood forecasting.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.39-50
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• 2006

The flood control offices of main rivers have used a storage function model to forecast flood stage in Korea and studies of flood forecasting actively have been done even now. On this account, the storage function model, which is used in flood control office, regression models and artificial neural network model are applied into flood forecasting of study watershed in this paper. The result obtained by each method are analyzed for the comparative study. In case of storage function model, this paper uses the representative parameters of the flood control offices and the optimized parameters. Regression coefficients are obtained by regression analysis and neural network is trained by backpropagation algorithm after selecting four events between 1995 to 2001. As a result of this study, it is shown that the optimized parameters are superior to the representative parameters for flood forecasting. The results obtained by multiple, robust, stepwise regression analysis, one of the regression methods, show very good forecasts. Although the artificial neural network model shows less exact results than the regression model, it can be efficient way to produce a good forecasts.

• Journal of Korea Water Resources Association
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• v.42 no.1
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• pp.61-73
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• 2009

To efficiently carry out the flood management of a multipurpose dam, two flood forecasting models are developed, each of which has the capabilities of forecasting upstream inflows and flood discharges downstream of a dam, respectively. The models are calibrated, validated, and evaluated by comparison of the observed and the runoff forecasts upstream and downstream of Namgang Dam. The upstream inflow forecasting model is based on the Grey system theory and employs the sixth order differential equation. By comparing the inflows forecasted by the models calibrated using different data sets with the observed in validation, the most appropriate model is determined. To forecast flood discharges downstream of a dam, a Grey model is integrated with a modified Muskingum flow routing model. A comparison of the observed and the forecasted values in validation reveals that the model can provide good forecasts for the dam's flood management. The applications of the two models to forecasting floods in real situations show that they provide reasonable results. In addition, it is revealed that to enhance the prediction accuracy, the models are necessary to be calibrated and applied considering runoff stages; the rising, peak, and falling stages.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.801-806
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• 2015

The incidence of flood damage by global climate change has increased recently. Because of the increased frequency of flooding in Korea, the technology of flood prediction and prevalence has developed mainly for large river watersheds. On the other hand, there is a limit on predicting flooding through the most present flood forecasting systems because local floods in small watersheds rise quite quickly with little or no advance warning. Therefore, this study estimated the flood warning rainfall using a flood forecasting model at the two alarm trigger points in the Suamcheon basin, which is an urban basin with backwater effects. The flood warning rainfall was estimated to be 25.4mm/120min ~ 78.8mm/120min for the low water alarm, and 68.5mm/120min ~ 140.7mm/120min for the high water alarm. The frequency of the flood warning rainfall is 3-years for the low water alarm, and 80-years for the high water alarm. The results of this analysis are expected to provide a basic database in forecasting local floods in urban watersheds. Nevertheless, more tests and implementations using a large number of watersheds will be needed for a practical flood warning or alert system in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1427-1431
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• 2007

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.669-673
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• 2008

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.