• Water for future
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• v.20 no.2
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• pp.107-116
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• 1987

The stochastic model has been developed for synthetic generation of hydrologic series that would be needed in the analysis, planning, design and operation of water resources system. In this study, after generating the yearly streamflows by multisite AR(1) model using the historical data in the Han River Basin, the monthly streamflows is generated by the disaggregation model. The model is verified of its applicability to domestic rivers, which is obtained through the statistical analysis and good ness of fit test using synthetic streamflows generated.

• Water for future
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• v.20 no.4
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• pp.257-266
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• 1987

The existing stochastic models for the data with hydrologic persistence can be classified into two categories; the short-term and long-term models.For the present study, the Hurst coefficients which are the dominant parameter in the Fast Fractional Gaussian Noise(FFGN)model, one of the long-term models. are estimated with historical annual and monthly streamflows. In order to verify the applicability of these estimators the statistical properties of the generated annual streamflows by FFGN model are compared with those of the historical annual streamflows. Then the generated annual streamflows by FFGN model are disaggregated into the monthly streamflows by disaggregation model at two sites, i.e. Waekman and Jindong, in the Nakdong River Basin. On the other hand, the monthly stream flows at the two sites were also generated by the two-site Matalas model which is one of the short-term models. To evaluate the applicability of the above models and to select the better model the statistical properties of the generated monthly streamflows by two models were compared with those of the historicals, respectively.

• Water for future
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• v.18 no.1
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• pp.75-83
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• 1985

The applicability of ARMA(1, 1) multiseason model, which is in the beginning stage of active researches in the field of synthetic generation is evaluated with the streamflow data at the Nakdong stage gauging station on the main stem of the Nakdong River. The method of parameter estimation for the modelis reviewed and the statistical analysis of the generated seasonal streamflows such as corrlogram analysis and the computation of moments is made. The results obtained by ARMA(1, 1) multiseason model are compared with the historical streamflow data and also with those by two other multiseason models, namely, Thomas-Fiering model and Matalas AR(1) multiseason model. The seasonal streamflows grnerated by three multiseason models were annually summed up to form respective annual flow series whose statistics were compared with those of the annual flow series generated by three annual models, namely, AR(1), Matalas AR(1), and ARMA(1, 1) annual models. The possibility of ARMA(1, 1) multiseason model for the simultaneous generation of seasonal and annual streamflows is also evaluated.

• Korean Journal of Agricultural Science
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• v.37 no.1
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• pp.105-112
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• 2010

The objective of this study is to develop a standardized model for analyzing water balances in large scaled water basin by considering agricultural water districts, and to evaluate the hydrological feasibility of applying this model to several water districts such as Nonbul, Geumbok, Daejeon 1, Daejeon 2, and Cheonggang in Geum river basin. Ten types of stream network were considered in developed model. Using this model, streamflows were simulated by major stations and water balances were analyzed by water districts. Simulated streamflows and measured streamflows were compared at check stations such as Gapcheon and Bugang stations in which Nash and Schcliffe's model efficiencies were 0.633, 0.902, respectively. This results showed its applicabilities to national water resources plan, rural water development plan, and total maximum daily load plan in Korea.

• Water for future
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• v.12 no.2
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• pp.63-69
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• 1979

The sequences of monthly streamflows constitute a non-statonary time series. The purely stochastic model has been applied to data generation of non-stationary time series. Tow different mothods--single site and multisite generation--have been used on the hydrologic time series. In this study the synthetic generation method by bivariate analysis, studied by Thomas Fiering, one of multi-site models, has been applied to the historical data on monthly streamflows at two sites in Nakdong River, and also for validity of this model the single site Thomas Fiering model applied. Through statistical analysis it has been shown that the performance of bivariate Thomas Fiering model was better than that of the other. By comparison of mean and standard deviaion between the historical and the generated, and cross correlogram interpretation, it has been known that the model used herein has good performance to simultaneously generate the monthly streamflows at two sites in a river hasin.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.3
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• pp.43-56
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• 1982

A recursive algorithms for prediction of streamflows by Kalman filtering theory and Self-tuning predictor based on the state space description of the dynamic systems have been studied and the applicabilities of the algorithms to the rainfall-runoff processes have been investigated. For the representation of the dynamics of the processes, a low-order ARMA process has been taken as the linear discrete time system with white Gaussian disturbances. The state vector in the prediction model formulated by a random walk process. The model structures have been determined by a statistical analysis for residuals of the observed and predicted streamflows. For the verification of the prediction algorithms developed here, the observed historical data of the hourly rainfall and streamflows were used. The numerical studies shows that Kalman filtering theory has better performance than the Self-tuning predictor for system identification and prediction in rainfall-runoff processes.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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• pp.383-388
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• 2001

• Korean Journal of Hydrosciences
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• v.4
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• pp.1-9
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• 1993

It is a major objective for the management and operation of water resources system to forecast streamflows. The applicability of artificial neural network model to hydrologic system is analyzed and the performance is compared by statistical method with observed. Multi-layered perception was used to model rainfall-runoff process at Pyung Chang River Basin in Korea. The neural network model has the function of learning the process which can be trained with the error backpropagation (EBP) algorithm in two phases; (1) learning phase permits to find the best parameters(weight matrix) between input and output. (2) adaptive phase use the EBP algorithm in order to learn from the provided data. The generalization results have been obtained on forecasting the daily and hourly streamflows by assuming them with the structure of ARMA model. The results show validities in applying to hydrologic forecasting system.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.3
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• pp.47-55
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• 1990

An adaptive algorithm was applied to forecast daily stream flows in real time using rainfall data. A three-component tank model was selected to simulate the flows and its time-variant parameters were self-calibrated with updated data using a parameter optimization scheme, golden section search method. The resulting adaptive model, APTANK, was applied to six watersheds, ranging from 0.47 to 33.62 km$^2$ size and the simulated daily streamflows were compared with the measured. The simulation results were in good agreement with the field data. APTANK is found to be applied to real-time flow simulation purposes such as a tool for irrigation water resources management and operations. The model is particularly good to simulate streamflows on dry days as compared to wet days having runoff-induced precipitation.

• Journal of the Korean Society of Safety
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• v.11 no.2
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• pp.122-127
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• 1996

The purpose of this study has been studied and investigated to prediction algorithms of the Kalman Filtering theory which are based on the state-vector description, including system identification, model structure determination, parameter estimation. And the prediction algorithms applied of rainfall-runoff process, has been worked out. The analysis of runoff process and runoff prediction algorithms of the river-basin established, for the verification of prediction algorithms by the Kalman Filtering theory, the observed historical data of the hourly rainfall and streamflows were used for the algorithms. In consisted of the above, Kalman Filtering rainfall-runoff model applied and analysised to Wi-Stream basin in Nak-dong River(Basin area : $472.53km^2$).