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Çѱ¹¼öÀÚ¿øÇÐȸ / v.41, no.9, 2008³â, pp.943-958
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( Identification of Uncertainty in Fitting Rating Curve with Bayesian Regression )
±è»ó¿í;À̱漺; ¼­¿ï´ëÇб³ BK21 ¾ÈÀüÇϰí Áö¼Ó°¡´ÉÇÑ »çȸ±â¹Ý°Ç¼³ »ç¾÷´Ü;¼­¿ï´ëÇб³ °ø°ú´ëÇÐ °Ç¼³.ȯ°æ°øÇкÎ;
 
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This study employs Bayesian regression analysis for fitting discharge rating curves. The parameter estimates using the Bayesian regression analysis were compared to ordinary least square method using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian regression are not significantly different. However, the difference between upper and lower limits are remarkably reduced with the Bayesian regression. Therefore, from the point of view of uncertainty analysis, the Bayesian regression is more attractive than the conventional method based on a t-distribution because the data size at the site of interest is typically insufficient to estimate the parameters in rating curve. The merits and demerits of the two types of estimation methods are analyzed through the statistical simulation considering heteroscedasticity. The validation of the Bayesian regression is also performed using real stage-discharge data which were observed at 5 gauges on the Anyangcheon basin. Because the true parameters at 5 gauges are unknown, the quantitative accuracy of the Bayesian regression can not be assessed. However, it can be suggested that the uncertainty in rating curves at 5 gauges be reduced by Bayesian regression.
 
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¼öÀ§-À¯·® °ü°è°î¼±;ºÒÈ®½Ç¼º;Bayesian ȸ±ÍºÐ¼®;t ºÐÆ÷;ºñµîºÐ»ê¼º;ÀϹÝÃÖ¼ÒÀڽ¹ý;discharge rating curve;uncertainty;Bayesian regression;t-distribution;heteroscedasticity;ordinary least square;
 
Çѱ¹¼öÀÚ¿øÇÐȸ³í¹®Áý / v.41, no.9, 2008³â, pp.943-958
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1226-6280
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO200828955287959)
¾ð¾î : Çѱ¹¾î
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø
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