|
|
Çѱ¹¼öÀÚ¿øÇÐȸ / v.41, no.1, 2008³â, pp.49-63
|
Bayesian MCMC¸¦ ÀÌ¿ëÇÑ Àú¼ö·® Á¡ ºóµµºÐ¼®: II. Àû¿ë°ú ºñ±³ºÐ¼®
( At-site Low Flow Frequency Analysis Using Bayesian MCMC: II. Application and Comparative Studies ) |
±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;
|
|
|
 |
|
|
ÃÊ ·Ï |
º» ¿¬±¸¿¡¼´Â Bayesian MCMC ¹æ¹ý°ú 2Â÷ ±Ù»ç½ÄÀ» ÀÌ¿ëÇÑ ÃÖ¿ìÃßÁ¤(Maximum Likelihood Estimation, MLE)¹æ¹ý ¹æ¹ýÀ» ÀÌ¿ëÇÏ¿© ³«µ¿° À¯¿ªÀÇ º»·ùÁöÁ¡ÀÎ ³«µ¿, ¿Ö°ü, °í·É±³, Áøµ¿ÁöÁ¡¿¡ ´ëÇÑ Á¡ ºóµµºÐ¼®À» ¼öÇàÇÏ°í ±× °á°ú·Î½á ºÒÈ®½Ç¼ºÀ» Æ÷ÇÔÇÑ ºóµµ°î¼±À» ÀÛ¼ºÇÏ¿´´Ù. Åë°èÀû ½ÇÇèÀ» ÅëÇÑ µÎ °¡Áö ÃßÁ¤¹æ¹ýÀÇ ºÐ¼®À» À§ÇÏ¿© ¸ÕÀú ÀÚ·áÀÇ ±æÀ̰¡ 100ÀÎ 8°³ÀÇ ÇÕ¼º À¯·®ÀÚ·á ¼ÂÀ» »ý¼ºÇÏ¿© ºñ±³ ¿¬±¸¸¦ ¼öÇàÇÏ¿´À¸¸ç, À̸¦ ÀÚ·á±æÀÌ 36ÀÎ ½ÇÃø À¯·® ÀÚ·áÀÇ ÃßÁ¤°á°ú¿Í ºñ±³ÇÏ¿´´Ù. Bayesian MCMC ¹æ¹ý¿¡ ÀÇÇÑ Æò±Õ°ª°ú 2Â÷ ±Ù»ç½ÄÀ» ÀÌ¿ëÇÑ Ãë¿ìÃßÁ¤¹æ¹ý¿¡ ÀÇÇÑ ¸ðµå¿¡¼ÀÇ 2¸ð¼ö Weibull ºÐÆ÷ÀÇ ¸ð¼ö ÃßÁ¤°ªÀº ºñ½ÁÇÑ °á°ú¸¦ º¸¿´À¸³ª, ºÒÈ®½Ç¼ºÀ» ³ªÅ¸³»´Â ÇÏÇѰª°ú »óÇѰªÀÇ Â÷ÀÌ´Â Bayesian MCMC ¹æ¹ýÀÌ 2Â÷ ±Ù»ç½ÄÀ» ÀÌ¿ëÇÑ Ãë¿ìÃßÁ¤¹æ¹ýº¸´Ù ºÒÈ®½Ç¼ºÀ» °¨¼Ò½ÃÄÑ ³ªÅ¸³»´Â °ÍÀ» ¾Ë ¼ö ÀÖ¾ú´Ù. ¶ÇÇÑ ½ÇÃø À¯·®ÀڷḦ ÀÌ¿ëÇÑ °á°ú, 2Â÷ ±Ù»ç½ÄÀ» ÀÌ¿ëÇÑ Ãë¿ìÃßÁ¤¹æ¹ýÀÇ °æ¿ì ÀÚ·áÀÇ ±æÀ̰¡ °¨¼ÒµÊ¿¡ µû¶ó ºÒÈ®½Ç¼ºÀÇ ¹üÀ§°¡ ÇÕ¼ºÀ¯·®ÀڷḦ »ç¿ëÇÑ °æ¿ì¿¡ ºñÇØ »ó´ëÀûÀ¸·Î Áõ°¡µÇÁö¸¸, Bayesian MCMC ¹æ¹ýÀÇ °æ¿ì¿¡´Â ÀÚ·áÀÇ ±æÀÌ¿¡ ´ëÇÑ ¿µÇâÀÌ °ÅÀÇ ¾ø´Ù´Â °á·ÐÀ» ¾òÀ» ¼ö ÀÖ¾ú´Ù. ±×·¯¹Ç·Î Àú¼ö·® ºóµµºÐ¼®À» ¼öÇàÇϱâ À§ÇØ ÃæºÐÇÑ ÀڷḦ È®º¸ÇÒ ¼ö ¾ø´Â ±¹³»ÀÇ »óȲÀ» °¨¾ÈÇÒ ¶§, À§¿Í °°Àº °á·ÐÀ¸·ÎºÎÅÍ Bayesian MCMC ¹æ¹ýÀÌ ºÒÈ®½Ç¼ºÀ» Ç¥ÇöÇϴµ¥ ÀÖ¾î¼ 2Â÷ ±Ù»ç½ÄÀ» ÀÌ¿ëÇÑ ÃÖ¿ìÃßÁ¤¹æ¹ý¿¡ ºñÇØ ÇÕ¸®ÀûÀÏ ¼ö ÀÖ´Ù´Â °á·ÐÀ» ¾òÀ» ¼ö ÀÖ¾ú´Ù. |
|
The Bayesian MCMC(Bayesian Markov Chain Monte Carlo) and the MLE(Maximum Likelihood Estimation) methods using a quadratic approximation are applied to perform the at-site low flow frequency analysis at the 4 stage stations (Nakdong, Waegwan, Goryeonggyo, and Jindong). Using the results of two types of the estimation method, the frequency curves including uncertainty are plotted. Eight case studies using the synthetic flow data with a sample size of 100, generated from 2-parmeter Weibull distribution are performed to compare with the results of analysis using the MLE and the Bayesian MCMC. The Bayesian MCMC and the MLE are applied to 36 years of gauged data to validate the efficiency of the developed scheme. These examples illustrate the advantages of the Bayesian MCMC and the limitations of the MLE based on a quadratic approximation. From the point of view of uncertainty analysis, the Bayesian MCMC is more effective than the MLE using a quadratic approximation when the sample size is small. In particular, the Bayesian MCMC is a more attractive method than MLE based on a quadratic approximation because the sample size of low flow at the site of interest is mostly not enough to perform the low flow frequency analysis. |
|
Ű¿öµå |
ºÒÈ®½Ç¼º;2Â÷ ±Ù»ç¸¦ ÀÌ¿ëÇÑ ÃÖ¿ìÃßÁ¤¹æ¹ý;2¸ð¼ö Weibull È®·üºÐÆ÷;ÇÕ¼º À¯·®ÀÚ·á;½ÇÃø À¯·®ÀÚ·á;ºóµµ°î¼±;Uncertainty;Bayesian MCMC;MLE using a quadratic approximation;2-paramter Weibull distribution;synthetic flow data;gauged data;frequency curve; |
|
|
|
 |
|
Çѱ¹¼öÀÚ¿øÇÐȸ³í¹®Áý / v.41, no.1, 2008³â, pp.49-63
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1226-6280
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO200804503863761)
¾ð¾î : Çѱ¹¾î |
|
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø |
|
|
|
|
|