• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.3
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• pp.112-126
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• 1996

This study was conducted to develop an optimal runoff bydrograph model by comparison of the peak discharge and time to peak between observed and simulated flows derived by four different models, that is, linear time-invariant, linear time-variant, nonlinear time-invariant and nonlinear time-variant models under the conditions of heavy rainfalls with regionally uniform rainfall intensity in short durations at nine small watersheds. The results obtained through this study can be summarized as follows. 1. Parameters for four models including linear time-invariant, linear time-variant, nonlinear time-invariant and nonlinear time-variant models were calibrated using a trial and error method with rainfall and runoff data for the applied watersheds. Regression analysis among parameters, rainfall and watershed characteristics were established for both linear time-invariant and nonlinear time-invariant models. 2. Correlation coefficients of the simulated peak discharge of calibrated runoff hydrographs by using four models were shown to be a high significant to the peak of observed runoff graphs. Especially, it can be concluded that the simulated peak discharge of a linear time-variant model is approaching more closely to the observed runoff hydrograph in comparison with those of three models in the applied watersheds. 3. Correlation coefficients of the simulated time to peak of calibrated runoff hydrographs by using a linear time-variant model were shown to be a high significant to the time to peak of observed runoff hydrographs than those of the other models. 4. The peak discharge and time to peak of simulated runoff hydrogaphs by using linear time-variant model are verified to be approached more closely to those of observed runoff hydrographs than those of three models in the applied watersheds. 5. It can be generally concluded that the shape of simulated hydrograph based on a linear time-variant model is getting closer to the observed runoff hydrograph than those of three models in the applied watersheds. 6. Simulated hydrographs using the nonlinear time-variant model which is based on more closely to the theoritical background of the natural runoff process are not closer to the observed runoff hydrographs in comparison with those of three models in the applied watersheds. Consequently, it is to be desired that futher study for the nonlinear time-variant model should be continued with verification using rainfall-runoff data of the other watersheds in addition to the review of analyical techniques.

• Water for future
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• v.19 no.3
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• pp.267-276
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• 1986

This study aims at the development of flood runoff model by comparing and analyzing nonlinear models with linear models in rier basins. The models which are used at the analysis are Nash model and Runoff function method as linear models, and Tank model and Storage function method as nonlinear models. The results, which are obtained from the analysis of these models by using hydrologic data of a representative basin in Nakdong river, Wi-chun basin, show that the peak time, peak flow and flood hydrogrphs by nonlinear models are better than those by linear models in comparison with observed ones, and that nonlinear models are suittable as flood runoff model.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• Journal of Korea Water Resources Association
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• v.33 no.S1
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• pp.101-110
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• 2000

This paper introduces the equivalent frequency response method(EFRM) into runoff analysis. This EFRM originally had been developed to analyze dynamic behavior of nonlinear elements such as threshold and saturation in control engineering. Many runoff models are described by nonlinear ordinary of partial differential equations This paper presents that these nonlinear differential equations can be converted into semi-linear ones based on EFRM. The word of "a semi-linear equation" means that the coefficients of derived equations depend on average rainfall.

• Proceedings of the Korea Water Resources Association Conference
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• 2000.05a
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• pp.1-2
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• 2000

This paper introduces the equivalent frequency response method (EFRM) into runoff analysis. This EFRM originally had been developed to analyze dynamic behavior of nonlinear elements such as threshold and saturation in control engineering. Many runoff models are described by nonlinear ordinary or partial differential equations. This paper presents that these nonlinear differential equations can be converted into semi-linear ones based on EFRM. The word of “a semi-linear equation” means that the coefficients of derived equations depend on average rainfall

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.235-235
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• 2015

This study investigates the impact of event characteristics on runoff dynamics during extreme flood events observed in a $8.5km^2$ experimental watershed located in South Korea. The 37 most extreme flood events with event rainfall in excess of 50 mm were analysed using an event-based rainfall-runoff model; the Revitalised Flood Hydrograph (ReFH) routinely used for design flood estimation in the United Kingdom. The ReFH model was fitted to each event in turn, and links were investigated between each of the two model parameters controlling runoff production and response time, respectively, and event characteristics such as rainfall depth, duration, intensity and also antecedent soil moisture. The results show that the structure of the ReFH model can effectively accommodate any nonlinearity in runoff production, but that the linear unit hydrograph fails to adequately represent a reduction in watershed response time observed for the more extreme events. By linking the unit hydrograph shape directly to rainfall depth, the consequence of the observed nonlinearity in response time is to increase design peak flow by between 50% for a 10 year return period, and up to 80% when considering the probable maximum flood (PMF).

• Korean Journal of Hydrosciences
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• v.5
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• pp.99-114
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• 1994

Three purposes of this study are as follows : The first was the development of the extension method for the limited data observed in an urban drainage basin. The second was the analysis of the correlation between storm water runoff and NPS(non-point source) Pollutant discharge. The last was the calculation of the monthly and annual specific NPS loads using the established correlation. The selected model was the SWMM(Storm Water Management Model) developed by the US EPA(Environmental Protection Agency). As a result of this study, the best correlation between storm water runoff and NPS pollutants discharge was produced by the nonlinear correlation between runoff rate(mm/hr) and specific loads rate(kg/ha) for all pollutants studied : SS, COD, BOD, and TN. The best correlation through the analysis based on evently total mass was made by the linear correlation between the by the nonlinear correlation for CASE2. The NPS annual specific loads for the urban basin studed were 4,993 kg/ha/year for SS, 775 kg/ha/year for BOD, 3,094 kg/ha/year for COD, 257 kg/ha/year for TN, respectively. And the proportion of the NPS annual specific loads to the total annual specific loads were 41 % for SS, 13 % for BOD, 29 % for COD, and 21 % for TN.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.13-25
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• 1998

It is necessary to develop methodologies for the application of artificial neural network into hydrologic rainfall-runoff process, although there is so much applicability by using the functions of associative memory based on recognition for the relationships between causes and effects and the excellent fitting capacity for the nonlinear phenomenon. In this study, some problems are presented in the application procedures of artificial neural networks and the simulation of runoff hydrograph experiences are reviewed with nonlinear functional approximator by artificial neural network for rainfall-runoff relationships in a watershed. which is regarded as hydrdologic black box model. The neural network models are constructed by organizing input and output patterns with the deserved rainfall and runoff data in Pyoungchang river basin under the assumption that the rainfall data is the input pattern and runoff hydrograph is the output patterns. Analyzed with the results. it is possible to simulate the runoff hydrograph with processing element of artificial neural network with any hydrologic concepts and the weight among processing elements are well-adapted as model parameters with the assumed model structure during learning process. Based upon these results. it is expected that neural network theory can be utilized as an efficient approach to simulate runoff hydrograph and identify the relationship between rainfall and runoff as hydrosystems which is necessary to develop and manage water resources.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.435-441
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• 2006

For the calibration of rainfall-runoff model, automatic calibration methods are used instead of manual calibration to obtain the reliable modeling results. When mathematical programming techniques such as linear programming and nonlinear programming are applied, there is a possibility to arrive at the local optimum. To solve this problem, genetic algorithm is introduced in this study. It is very simple and easy to understand but also applicable to any complicated mathematical problem, and it can find out the global optimum solution effectively. The objective of this study is to develope a parameter optimization program that integrate a genetic algorithm and a rainfall-runoff model. The program can calibrate the various parameters related to the runoff process automatically. As a rainfall-runoff model, SWMM is applied. The automatic calibration program developed in this study is applied to the Jangcheon watershed flowing into the Youngrang Lake that is in the eutrophic state. Runoff surveys were carried out for two storm events on the Jangcheon watershed. The peak flow and runoff volume estimated by the calibrated model with the survey data shows good agreement with the observed values.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.4
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• pp.1-10
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• 2010

In this study, two estimation equations for preparing stream data for distributed storm runoff model were developed by analyzing the nonlinear relation between upstream flow-length and stream width, and between upstream flow-length and stream bed-slope. The equations for stream cell were tested in Chungjudam watershed (6,661 $km^2$) using KIMSTORM. Six storm events occurring between 2003 and 2008 were selected for the model calibration and verification before the test of equations. The average values of the Nash-Sutcliffe model efficiency (ME), the volume conservation index (VCI), the relative error of peak runoff rate (EQp), and the difference of time to peak runoff (DTp) were 0.929, 1.035, 0.037, and -0.406 hr for the calibrated four storm events and 0.956, 0.939, 0.055, and 0.729 hr for the two verified storm events respectively. The estimation equations were tested to the storm events, and compared the flood hydrograph. The test result showed that the estimation equation of stream width reduced the peak runoff and delaying the time to peak runoff, and the estimation equation of stream bed-slope showed the opposite results.