• Journal of Korea Water Resources Association
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• v.50 no.5
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• pp.335-346
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• 2017

This study generated the radar-based forecasted rainfall using spatial-scale decomposition method (SCDM) and evaluated the hydrological applicability with forecasted rainfall by KMA (MAPLE, KONOS) in terms of urban flood forecasting. SCDM is to separate the small-scale field (convective cell) and large-scale field (straitform cell) from radar rainfield. And each separated field is forecasted by translation model and storm tracker nowcasting model for improvement of QPF accuracy. As the evaluated results of various QPF for three rainfall events in Seoul and Metropolitan area, proposed method showed better prediction accuracy than MAPLE and KONOS considering the simplicity of the methodology. In addition, this study assessed the urban hydrological applicability for Gangnam basin. As the results, KONOS simulated the peak of water depth more accurately than MAPLE and SCDM, however cannot simulated the timeseries pattern of water depth. In the case of SCDM, the quantitative error was larger than observed water depth, but the simulated pattern was similar to observation. The SCDM will be useful information for flood forecasting if quantitative accuracy is improved through the adjustment technique and blending with NWP.

• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.81-88
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• 2018

The rainfall-runoff model made of sewer networks in the urban area is vast and complex, making it unsuitable for real-time urban flood forecasting. Therefore, the rainfall-runoff model is constructed and simplified using the sewer network of Daerim baisn. The network simplification process was composed of 5 steps based on cumulative drainage area and all parameters of SWMM were calculated using weighted area. Also, in order to estimate the optimal simplification range of the sewage network, runoff and flood analysis was carried out by 5 simplification ranges. As a result, the number of nodes, conduits and the simulation time were constantly reduced to 50~90% according to the simplification ranges. The runoff results of simplified models show the same result before the simplification. In the 2D flood analysis, as the simplification range increases by cumulative drainage area, the number of overflow nodes significantly decreased and the positions were changed, but similar flooding pattern was appeared. However, in the case of more than 6 ha cumulative drainage area, some inundation areas could not be occurred because of deleted nodes from upstream. As a result of comparing flood area and flood depth, it was analyzed that the flood result based on simplification range of 1 ha cumulative drainage area is most similar to the analysis result before simplification. It is expected that this study can be used as reliable data suitable for real-time urban flood forecasting by simplifying sewer network considering SWMM parameters.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.363-363
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• 2023

Artificial Neural Networks (ANN) is a powerful tool for addressing various practical problems and it has been extensively applied in areas of water resources. In this study, Artificial Neural Networks (ANNs) were developed for flood forecasting at specific locations on the Han River. The Partial Mutual Information (PMI) technique was used to select input variables for ANNs that are neither over-specified nor under-specified while adequately describing the underlying input-output relationships. Historical observations including discharges at the Paldang Dam, flows from tributaries, water levels at the Paldang Bridge, Banpo Bridge, Hangang Bridge, and Junryu gauge station, and time derivatives of the observed water levels were considered as input candidates. Lagged variables from current time t to the previous five hours were assumed to be sufficient in this study. A three-layer neural network with one hidden layer was used and the neural network was optimized by selecting the optimal number of hidden neurons given the selected inputs. Given an ANN architecture, the weights and biases of the network were determined in the model training. The use of PMI-based input variable selection and optimized ANNs for different sites were proven to successfully predict water levels during flood periods.

• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.235-242
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• 1998

The objective of this study is to develope a predictive model for flood forecasting in the tidal reaches of the Nakdong river and to analyze the tidal effects of major flood forecasting station of the Nakdong river by using the hydraulic flood routing. In the calibration process the optimum roughness coefficients as functions of channel reach and discharge were determined and the calibration results suggest that the unsteady hydraulic flood routing model simulated with the optimum roughness coefficients showed close agreement between the calculated and observed stage.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.3
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• pp.60-66
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• 1994

This paper is to validate the proposed models for the real-time forecasting for the Keum river estuary dam such as tidal-level forecasting model, one-dimensional unsteady flood routing model, and Kalman filter models. The tidal-level forecasting model was based on semi-range and phase lag of four tidal constituents. The dynamic wave routing model was based on an implicit finite difference solution of the complete one-dimensional St. Venant equations of unsteady flow. The Kalman filter model was composed of a processing equation and adaptive filtering algorithm. The processng equations are second ordpr autoregressive model and autoregressive moving average model. Simulated results of the models were compared with field data and were reviewed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2687-2692
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• 2010

There have been performed many researched for flood magnitude analysis, for example, the Flood-Duration-Frequency relations in the west. Because flood water stage data are more available rather than flood amount data at flood gauge stations of Korea, this study developed Flood water level-Duration-Frequency (Fwl-D-F) curves using rainfall Intensity-Duration-Frequency(I-D-F) curves for the quantitative flood risk assessment in urban watersheds. Fwl-D-F curve is made from water level data for 18 years at Joongrayng bridge station of Joongrayng River basin in Han River drainage area. Fwl-D-F curve can estimate the occurrence frequency for a certain flood elevation, which can be used for urban flood forecasting. It is expected that the flood elevation can be estimated from the forecasted rainfall data using both Fwl-D-F and I-D-F curves.

• 한국전산유체공학회:학술대회논문집
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• 2008.08a
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• pp.38.4-38.4
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• 2008

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.87-98
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• 2000

The objective of this study is to test the flood forecasting capability of TOPMODEL on a single watershed in Korea. The selected study area is the Soyang River basin with outlet at Soyang Dam site. The three daily hydrographs and the three hourly flood events during 1990~1996 are selected for model calibrations and performance tests. The model parameters are estimated on 1990 daily event by manual fitting technique and the effects of topographic index distribution to river flow simulations are investigated on the study area. The model performance on correlation coefficient between the observed and the simulated flows for the verification periods are above 0.77 on the 95-, 96-daily events, while above 0.87 for 90-, 95-, 96-hourly events. By the consideration of flood flow characteristics in Korea, the physical interpretation of the model concept, and the model performance, it can be concluded that the TOPMODEL is feasible as a flood forecasting model in Korea. Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.27-38
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• 2006

The storage function model (SFM) has been used for the flood forecasting in Korea. The SFM has a simple calculation process and it is known that the model is more reasonable than linear model because it considers non-linearity of flood runoff. However, the determination of parameters is very difficult. In general, the trial and error method which is an manual calibration by the decision of a model manager. This study calibrated the parameters by the trial and error method and optimization technique. The calibrated parameters were compared with the representative parameters which are used in the Flood Control Centers in Korea. Also, the evaluation indexes on objective functions and calibration methods for the comparative analysis of simulation efficiency. As a result, the Genetic Algorithm showed the smallest variation in objective functions and, in this study, it is known that the objective function of SSR (Sum of Squared of Residual) is the best one for the flood forecasting.