• Water for future
• /
• v.18 no.3
• /
• pp.265-270
• /
• 1985

The prediction of a design-flood hydrograph at a particular site on a river may be based on the derivation of discharge or stage hydrograph at an upstream section, togeater with a method to route this hydrograph along the rest of river. On the other hand, flood routing methods provide a useful tool for the analysis of flooding in all but the smaller catchment, and these methods are largely stored into hydrological method and hydraulic method. Although the Muskingum Method as a hydrological method ignores dynamic effects on the flood wave, Muskingum-Cunge Method based on hydraulic method is possible to improve the method so that it gives a good approximation to the solution of the linear convective-diffusion equation. This is made on the basis of the finite diffeience equation for the Muskingum Method. In the study, the outflows predicted by Muskingum-Cunge Method are campared with the observed outflows of the Pyung Chang River.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.31 no.1
• /
• pp.117-130
• /
• 1989

In this study, an implicit one-dimensional model, DWRM(Dynamic Wave Routing Model) was developed by using the four-point weighted difference method. By applying the developed model to the Keum River, the parameters were calibrated and the model applicability was tested through the comparison between observed and computed water levels. In addition, the effects of the construction of an estuary dam to the flood wave were estimated as a result of the model application. The results of the study can be summarized as follows; 1. The roughness coefficients were evaluated by comparison between observed and computed water level at Jindu, Gyuam and Ganggyeung station in 1985. The Root Mean Squares for water level differences between observed and computed values were 0.10, 0.11, 0. 29m and the differences of peak flood levels were 0.07, 0.02, 0. 07m at each station. Since the evaluated roughness coefficients were within the range of 0.029-0.041 showing the realistic value for the general condition of rivers, it can be concluded that the calibration has been completed. 2. By the application of model using the calibrated roughness coefficients, the R. M. S. for water level differences were 0.16, 0.24, 0. 24m and the differences of peak flood level were 0.17, 0.13,0.08 m at each station. The arrival time of peak flood at each station and the stage-discharge relationship at Gongju station agreed well with the observed values. Therefore, it was concluded that the model could be applied to the Keum River. 3. The model was applied under conditions before and after the construction of the estuary dam. The 50-year frequency flood which had 7, 800m$^3$/sec of peak flood was used as the upstream condition, and the spring tide and the neap tide were used as the downstream condition. As the results of the application, no change of the peak flood level was showed in the upper reaches of 19.2km upstream from the estuary dam. For areas near 9.6km upstream from the estuary dam, the change of the peak flood level under the condition before and after the construction was 0. 2m. However considering the assumptions for the boundary conditions of downstream, the change of peak flood level would be decreased.

• Journal of Korea Water Resources Association
• /
• v.33 no.2
• /
• pp.247-262
• /
• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.

• Journal of Wetlands Research
• /
• v.13 no.3
• /
• pp.547-565
• /
• 2011

Different types of schemes have been used in stage prediction involving conceptual and physical models. Nevertheless, none of these schemes can be considered as a single superior model. To overcome disadvantages of existing physics based rainfall-runoff models for stage predicting because of the complexity of the hydrological process, recently the data-derived models has been widely adopted for predicting flood stage. The objective of this study is to evaluate model performance for stage prediction of the Neuro-Fuzzy and regression analysis stage prediction models in these data-derived methods. The proposed models are applied to the Wangsukcheon in Han river watershed. To evaluate the performance of the proposed 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 Neuro-Fuzzy stage prediction model can carry out the river flood stage prediction more accurately than the regression analysis stage prediction model. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.1
• /
• pp.495-505
• /
• 2014

The retention basin is a river-facility for the flood mitigation by storing the river flow temporarily. The new 3 retention basins are installed in these regions YeoJu, NaJu, YoungWol by the Large River Management Project. In this study, 1D and 2D numerical flow simulation are conducted to evaluate the reduction effect of the peak flood stage for the YeoJu retention basin. HEC-RAS and FLDWAV models are used for 1D simulation with the option of retention basin. CCHE2D model is used for 2D simulation with the same hydrograph used in 1D simulation. It is verified that the peak flood stage is reduced very largely about 0.13 m near the overtopping section of the levee in 1D simulation. It is verified that the peak flood stage is reduced very largely about 0.20 m at the upstream-end of the simulated reach in 2D simulation. 2D simulation for the retention basin is more reasonable because physical characteristics of topography in the model, and also more advantageous for the evaluation of the flow characteristics of the in- and outside of the retention basin on the results of simulation of this study.

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

• Journal of Korea Water Resources Association
• /
• v.39 no.4 s.165
• /
• pp.347-362
• /
• 2006

In this study, a 2-D flood inundation model was developed to evaluate the impact of levee failure in a natural basin for flood analysis. The model was applied to analyze the inundation flow from the levee break of Gamcheon river during the typhoon Rusa on October 31 through September 1, 2002. To verify the simulated results, wide range field surveys have been performed including the collection of NGIS database, land use condition, flooded area, and flow depths. Velocity distributions and inundation depths were presented to demonstrate the robustness of the model. Model results have good agreements with the observed data in terms of flood level and flooded area. The model is able to compute maximum stage and peak discharge efficiently in channel and protected lowland. Methodology considering radar-rainfall estimation using cokriging scheme, flood-runoff and inundation analysis in this study will contribute to the establishment of the national integrated flood disaster prevention system and the river or protect lowland management system.

• Ocean Systems Engineering
• /
• v.9 no.3
• /
• pp.287-328
• /
• 2019

Comprehensive understanding of the flood risk assessments via frequency analysis often demands multivariate designs under the different notations of return periods. Flood is a tri-variate random consequence, which often pointing the unreliability of univariate return period and demands for the joint dependency construction by accounting its multiple intercorrelated flood vectors i.e., flood peak, volume & durations. Selecting the most parsimonious probability functions for demonstrating univariate flood marginals distributions is often a mandatory pre-processing desire before the establishment of joint dependency. Especially under copulas methodology, which often allows the practitioner to model univariate marginals separately from their joint constructions. Parametric density approximations often hypothesized that the random samples must follow some specific or predefine probability density functions, which usually defines different estimates especially in the tail of distributions. Concentrations of the upper tail often seem interesting during flood modelling also, no evidence exhibited in favours of any fixed distributions, which often characterized through the trial and error procedure based on goodness-of-fit measures. On another side, model performance evaluations and selections of best-fitted distributions often demand precise investigations via comparing the relative sample reproducing capabilities otherwise, inconsistencies might reveal uncertainty. Also, the strength & weakness of different fitness statistics usually vary and having different extent during demonstrating gaps and dispensary among fitted distributions. In this literature, selections efforts of marginal distributions of flood variables are incorporated by employing an interactive set of parametric functions for event-based (or Block annual maxima) samples over the 50-years continuously-distributed streamflow characteristics for the Kelantan River basin at Gulliemard Bridge, Malaysia. Model fitness criteria are examined based on the degree of agreements between cumulative empirical and theoretical probabilities. Both the analytical as well as graphically visual inspections are undertaken to strengthen much decisive evidence in favour of best-fitted probability density.

• Journal of Korea Water Resources Association
• /
• v.30 no.4
• /
• pp.335-346
• /
• 1997

This study aims at examining closely the scour around a pier due to irregular water stage changes during flood. At the Sangye bridge is located lowermost downstream of the Bocheong stream in the Kum River, the IHP experimental watershed. For this purpose, we have analyzed the change of scour depths due to stage hydrographs of experimental basin by a simulation. To examine the scour phenomenon around a pier due to irregular stage change in flood, we have analyzed the change of scour depth corresponding to stage hydrograph of field watershed after verification of model channel. From this study, the following conclusions are made: First, in case of predicting the maximum scour depth around a pier with stage hydrograph in the state of steady flow, we should choose the highest stage. Second, after increasing the stage, the equilibrium scour depth became smaller than the maximum scour depth. Therefore, in case of estimating the maximum scour depth in rivers, it is recommended that we should consider additional scour depth with is reduced by infilling the sediments.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.56 no.2
• /
• pp.1-9
• /
• 2014

A modeling system is constructed by integrating an one-dimensional unsteady flow simulation model and a hydrologic model to simulate flood flows in drainage channel networks of paddy field districts. The modeling system's applicability is validated by simulating flood discharges from a paddy field district, which consists of nine paddy fields and one drainage channel. The simulation results are in good agreement with the observed. Particularly, in the verification stage, the relative errors of peak flows and peak depths between the observed and simulated hydrographs range 8.96 to 10.26 % and -10.26 to 2.97 %, respectively. The modeling system's capability is compared with that of a water balance equation-based model; it is revealed that the modeling system's accuracy is superior to the other model. In addition, the simulations of flood discharges from large-sized paddy fields through drainage channels show that the flood discharge patterns are affected by drainage outlet management for paddy fields and physical characteristics of the drainage channels. Finally, it is concluded that to efficiently design drainage channel networks, it is necessary to analyze the results from simulating flood discharges of the drainage channel networks according to their physical characteristics and connectivities.