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Çѱ¹¼öÀÚ¿øÇÐȸ / v.27, no.1, 1994³â, pp.89-99
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°¿ì-À¯Ãâ¸ðÇüÀ» ÀÌ¿ëÇÑ ½Ç½Ã°£ È«¼ö¿¹Ãø(I) : À̷аú ¸ðÇüÈ
( Real-Time Flood Forecasting Using Rainfall-Runoff Model(I) : Theory and Modeling ) |
| Á¤µ¿±¹;À̱漺; Çѳ²´ëÇб³ Åä¸ñ°øÇаú;¼¿ï´ëÇб³ Åä¸ñ°øÇаú;
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| Flood forecasting in Korea has been based on the off-line parameter estimation method. But recent flood forecasting studies explore on-line recursive parameter estimation algorithms. In this study, a simultaneous adaptive estimation of system states and parameters for rainfall-runoff model is investigated for on-line real-time flood forecasting and parameter estimation. The proposed flood routing system is composed of Flood forecasting in Korea has been based on the off-line parameter estimation method. But recent flood forecasting studies explore on-line recursive parameter estimation algorithms. In this study, a simultaneous adaptive estimation of system states and parameters for rainfall-runoff model is investigated for on-line real-time flood forecasting and parameter estimation. The proposed flood routing system is composed of ©ª-index in the assessment of effective rainfall and the cascade of nonlinear reservoirs accounting for translation effect in flood routing. To combine the flood routing model with a parameter estimation model, system states and parameters are treated with the extended state-space formulation. Generalized least squares and maximum a posterior estimation algorithms are comparatively examined as estimation techniques for the state-space model. The sensitivity analysis is to investigate the identifiability of the parameters. The index of sensitivity used in this study is the covariance matrix of the estimated parameters.-index in the assessment of effective rainfall and the cascade of nonlinear reservoirs accounting for translation effect in flood routing. To combine the flood routing model with a parameter estimation model, system states and parameters are treated with the extended state-space formulation. Generalized least squares and maximum a posterior estimation algorithms are comparatively examined as estimation techniques for the state-space model. The sensitivity analysis is to investigate the identifiability of the parameters. The index of sensitivity used in this study is the covariance matrix of the estimated parameters. |
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Çѱ¹¼öÀÚ¿øÇÐȸÁö / v.27, no.1, 1994³â, pp.89-99
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1738-9488
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO199411920094783)
¾ð¾î : Çѱ¹¾î |
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| ³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø |
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