¶óÆæÆ®¦¢Ä«Æä¦¢ºí·Î±×¦¢´õº¸±â
¾ÆÄ«µ¥¹Ì Ȩ ¸í»çƯ°­ ´ëÇבּ¸½Ç޹æ Á¶°æ½Ç¹« µ¿¿µ»ó°­ÀÇ Çѱ¹ÀÇ ÀüÅëÁ¤¿ø ÇÐȸº° ³í¹®
ÇÐȸº° ³í¹®

Çѱ¹°Ç¼³°ü¸®ÇÐȸ
Çѱ¹°ÇÃà½Ã°øÇÐȸ
Çѱ¹µµ·ÎÇÐȸ
Çѱ¹»ý¹°È¯°æÁ¶ÀýÇÐȸ
Çѱ¹»ýÅÂÇÐȸ
Çѱ¹¼öÀÚ¿øÇÐȸ
Çѱ¹½Ä¹°ÇÐȸ
Çѱ¹½Ç³»µðÀÚÀÎÇÐȸ
Çѱ¹ÀÚ¿ø½Ä¹°ÇÐȸ
Çѱ¹ÀܵðÇÐȸ
Çѱ¹Á¶°æÇÐȸ
Çѱ¹Áö¹Ý°øÇÐȸ
Çѱ¹ÇÏõȣ¼öÇÐȸ
Çѱ¹È¯°æ»ý¹°ÇÐȸ
Çѱ¹È¯°æ»ýÅÂÇÐȸ

Çѱ¹¼öÀÚ¿øÇÐȸ / v.42, no.10, 2009³â, pp.857-866
¿¬ÃÖ´ë°­¿ì·®ÀÇ ´ëǥȮ·üºÐÆ÷Çü °áÁ¤À» À§ÇÑ Jackknife±â¹ýÀÇ Àû¿ë
( Application of Jackknife Method for Determination of Representative Probability Distribution of Annual Maximum Rainfall )
ÀÌÀçÁØ;ÀÌ»ó¿ø;°ûâÀç; ±¹¸³±Ý¿À°ø°ú´ëÇб³ Åä¸ñȯ°æ°øÇкÎ;±¹¸³¹æÀ翬±¸¼Ò;±¹¸³±Ý¿À°ø°ú´ëÇб³ ´ëÇпø Åä¸ñ°øÇаú;
 
ÃÊ ·Ï
º» ¿¬±¸¿¡¼­´Â Àü±¹ÀÇ 30³â ÀÌ»óÀÇ °­¿ì°üÃø±â·ÏÀ» º¸À¯Çϰí ÀÖ´Â ±â»óû »êÇÏ 56°³ °­¿ì°üÃø¼ÒÀÇ ¿¬ ÃÖ´ëÄ¡ °­¿ìÀÚ·áµé·ÎºÎÅÍ È®·üºÐÆ÷Çü¿¡ ´ëÇÏ¿© ¸ð¸àÆ®¹ý, ÃÖ¿ìÃßÁ¤¹ý, È®·ü°¡Á߸ð¸àÆ®¹ýÀ» ÀÌ¿ëÇÏ¿© ¸ð¼ö¸¦ ÃßÁ¤Çϰí, ±× ¸ð¼öÀÇ ¹üÀ§¿Í È®·üº¯¼öÀÇ ¹üÀ§¿¡ ´ëÇÑ ÀûÁ¤¼ºÀ» ¾Ë¾Æº¸¾Ò´Ù. ÀûÁ¤¼ºÀÌ ÀÖ´Â ¸ð¼ö¸¦ ´ë»óÀ¸·Î ÀûÇÕµµ °ËÁ¤¹ýÀÎ x$^2$-°ËÁ¤, K-S°ËÁ¤, Cramer von Mises (CVM)°ËÁ¤, Probability Plot Correlation Coefficient (PPCC) °ËÁ¤À» ½Ç½ÃÇÑ °á°ú Áß, ÃÖ±Ù ¿¬±¸¿¡¼­ ¸¹ÀÌ ÀÌ¿ëµÇ°í ÀÖ°í Ç¥º»ÀÚ·áÀÇ Å©±â°¡ À۰ųª ¿Ö°îµÈ ÀÚ·áÀÏ °æ¿ì¿¡µµ ºñ±³Àû ¾ÈÁ¤ÀûÀÎ °á°ú¸¦ ¾òÀ» ¼ö ÀÖ´Â È®·ü°¡Á߸ð¸àÆ®¹ý°ú »ó°ü°è¼ö¿¡ ÀÇÇÑ °ËÁ¤ÀÎ PPCC°ËÁ¤À» Åë°úÇÑ ºÐÆ÷ÇüÀ» ¿ì¼±ÀûÀ¸·Î ÀûÇÕµµ Æò°¡ ´ë»ó ºÐÆ÷ÇüÀ¸·Î ¼±Á¤ÇÏ¿´´Ù. ¼±Á¤µÈ ºÐÆ÷ÇüÀ» ´ë»óÀ¸·Î ÀûÇÕµµ Æò°¡±âÁØÀÎ SLSC, MLL, AIC¸¦ Àû¿ëÇÏ¿© ÀûÇÕµµ Æò°¡¸¦ ½Ç½ÃÇÏ¿© ´ëǥȮ·üºÐÆ÷Çü È帱ºÀ» ÃßÃâÇÏ¿´´Ù. ´ëǥȮ·üºÐÆ÷Çü È帱ºÀ¸·Î ¼±Á¤µÈ È®·üºÐÆ÷Çü¿¡ ´ëÇÏ¿© resampling¹æ¹ýÀÎ Jackknife±â¹ýÀ» Àû¿ëÇÏ¿© º¯µ¿¼ºÀ» ÆÄ¾ÇÇϰí, º¯µ¿¼ºÀÌ °¡Àå ÀÛ°Ô ³ªÅ¸³­ ºÐÆ÷ÇüÀ» ±× ÁöÁ¡ÀÇ ´ëǥȮ·üºÐÆ÷ÇüÀ¸·Î °áÁ¤ÇÏ¿´´Ù. º» ³í¹®¿¡¼­´Â ºÐ¼® °á°úÀÇ ºÐ·®À» °¨¾ÈÇÏ¿© ´ëÇ¥ÀûÀ¸·Î ¼­¿ï, °­¸ª, ´ë±¸, ÀüÁÖ, ºÎ»ê ÁöÁ¡¿¡ ´ëÇØ ÀÛ¼ºÇÏ¿´À¸¸ç, È®·ü°­¿ì·®ÀÇ º¯µ¿¼ºÀÌ °¡Àå ÀÛÀº È®·üºÐÆ÷ÇüÀ» 56°³ ÁöÁ¡ÀÇ °¢ ÁöÁ¡ ´ëǥȮ·üºÐÆ÷ÇüÀ¸·Î Á¦½ÃÇÏ¿´À¸¸ç, Gumbel ºÐÆ÷(GUM)ÀÇ ¼±Á¤ ºñÀ²ÀÌ Áö¼Ó±â°£ 12½Ã°£, 24½Ã°£¿¡ ´ëÇØ °¢°¢ 41 %, 32 %·Î °¡Àå ³ô°Ô ³ªÅ¸³µ´Ù. º» ¿¬±¸¿¡¼­´Â ÀûÇÕµµ Æò°¡¸¦ ÇÔ¿¡ À־ °´°üÀû Á¤·®È­°¡ °¡´ÉÇÑ ¼¼ °¡Áö ±âÁذú Jackknife±â¹ýÀ» ÀÌ¿ëÇÑ »õ·Î¿î È®·üºÐÆ÷Çü ¼±Á¤ÀÇ °¡´É¼ºÀ» Á¦½ÃÇÏ¿´´Ù.
In this study, basic data is consisted annual maximum rainfall at 56 stations that has the rainfall records more than 30years in Korea. The 14 probability distributions which has been widely used in hydrologic frequency analysis are applied to the basic data. The method of moments, method of maximum likelihood and probability weighted moments method are used to estimate the parameters. And 4-tests (chi-square test, Kolmogorov-Smirnov test, Cramer von Mises test, probability plot correlation coefficient (PPCC) test) are used to determine the goodness of fit of probability distributions. This study emphasizes the necessity for considering the variability of the estimate of T-year event in hydrologic frequency analysis and proposes a framework for evaluating probability distribution models. The variability (or estimation error) of T-year event is used as a criterion for model evaluation as well as three goodness of fit criteria (SLSC, MLL, and AIC) in the framework. The Jackknife method plays a important role in estimating the variability. For the annual maxima of rainfall at 56 stations, the Gumble distribution is regarded as the best one among probability distribution models with two or three parameters.
 
Ű¿öµå
¿¬ÃÖ´ë°­¿ì·®;ÀûÇÕµµ Æò°¡±âÁØ;ºóµµÇؼ®;Jackknife±â¹ý;annual maximum rainfall;goodness of fit criteria;frequency analysis;Jackknife method;
 
Çѱ¹¼öÀÚ¿øÇÐȸ³í¹®Áý / v.42, no.10, 2009³â, pp.857-866
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1226-6280
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO200932848675458)
¾ð¾î : Çѱ¹¾î
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø
¸ñ·Ïº¸±â
ȸ»ç¼Ò°³ ±¤°í¾È³» ÀÌ¿ë¾à°ü °³ÀÎÁ¤º¸Ãë±Þ¹æÄ§ Ã¥ÀÓÀÇ ÇѰè¿Í ¹ýÀû°íÁö À̸ÞÀÏÁÖ¼Ò ¹«´Ü¼öÁý °ÅºÎ °í°´¼¾ÅÍ
   

ÇÏÀ§¹è³ÊÀ̵¿