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Çѱ¹¼öÀÚ¿øÇÐȸ / v.41, no.10, 2008³â, pp.995-1007
³ªÁÖÁöÁ¡ÀÇ °­¿ì-À¯Ãâ ÇØ¼®À» À§ÇÑ ÃÖÀûÀÇ SOM ±¸Á¶ °áÁ¤
( Determination of the Optimized Structure of Self-Organizing Map for the Rainfall-Runoff Analysis in Naju )
±è¿ë±¸;Áø¿µÈÆ;¹Ú¼ºÃµ;Á¤Ãµ¸®; µ¿½Å´ëÇб³ Åä¸ñ°øÇаú;µ¿½Å´ëÇб³;µ¿½Å´ëÇб³ Åä¸ñ°øÇаú;µ¿½Å´ëÇб³ °ø°ú´ëÇÐ Åä¸ñ°øÇаú;
 
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Àΰø½Å°æ¸Á ÀÌ·ÐÀ» ÀÌ¿ëÇÏ¿© °­ÇÑ ºñ¼±Çü¼ºÀÇ °æÇâÀ» º¸À̰í ÀÖ´Â °­¿ì-À¯Ãâ°£ÀÇ °ü°è¸¦ ¸ðÇüÈ­Çϱâ À§ÇÑ ¿¬±¸µéÀº ¿¹Ãø»Ó¸¸ÀÌ ¾Æ´Ï¶ó ´ë»óÀÚ·áµéÀÇ ¾ç»óÀ» ºÐ·ùÇÏ¿© ±× Ư¼ºÀ» ºÐ¼®ÇÏ´Â µ¥¿¡µµ ÀÌ¿ëµÇ°í ÀÖ´Ù. ÀÌ¿Í °°Àº ÆÐÅϺзù¸¦ À§ÇÑ SOM(Self-Organizing Map: SOM)ÀÇ ¿¬±¸ °á°ú¸¦ °ËÅäÇØº¸¸é SOM ÈÆ·ÃÀ» À§ÇÑ ÁöµµÅ©±â ¹× ¹è¿­ÀÇ °áÁ¤Àº SOM ¼º´É¿¡ Å« ¿µÇâÀ» ¹ÌÄ¡´Â °ÍÀ¸·Î º¸°íµÇ°í ÀÖÀ¸³ª ÁöµµÅ©±â °áÁ¤½Ã ÁöµµÀÇ Á¾¹æÇâ Å©±â¿Í Ⱦ¹æÇâ Å©±â¸¦ °áÁ¤ÇÒ ¼ö ÀÖ´Â È®Á¤·ÐÀûÀÎ ¹æ¹ýÀ̳ª À̷нÄÀÌ ¾ø°í, Áöµµ¹è¿­Àº ÁÖ·Î À°°¢Çü ¹è¿­(hexagonal array)À» ÀÌ¿ëÇÏ¿© Àû¿ëÇϰí ÀÖ´Ù. µû¶ó¼­ º» ¿¬±¸¿¡¼­´Â ¿µ»ê°­ ³ªÁÖÁöÁ¡À» ´ë»óÀ¸·Î °­¿ì-À¯Ãâ°ü°èÀÇ ºÐÇÒÆ¯¼ºÀ» ³ªÅ¸³»´Â ÁöµµÅ©±â¿Í ¹è¿­À» º¹ÇÕÀûÀ¸·Î °ËÅäÇÏ¿© ³ªÁÖÁöÁ¡ÀÇ °­¿ì-À¯Ãâ ÇØ¼®À» À§ÇÑ ÀûÀýÇÑ Áöµµ±¸Á¶¸¦ °áÁ¤ÇÏ¿´´Ù. ±× °á°ú 8°³ÀÇ ÆÐÅÏÀ¸·Î ±¸ºÐµÈ ÁöµµÅ©±â 20$ imes$16ÀÇ À°°¢Çü¹è¿­ ±¸Á¶°¡ ³ªÁÖÁöÁ¡ÀÇ °­¿ì-À¯ÃâÇØ¼®À» À§ÇÑ ÀûÀýÇÑ Áöµµ±¸Á¶·Î °áÁ¤µÇ¾ú´Ù.
Studies on modeling the rainfall-runoff relationship which shows nonlinear trend strongly use artificial neural networks theory not only for the prediction but also for the characteristics analysis of the data used by pattern classification. For the pattern classification, the results from Self-Organizing Map (SOM) mention that the map size and array for the SOM training have significantly influenced on the SOM performance. Since there is no deterministic method or theoretical equation to determine the number of rows and columns for the map size, hexagonal array is generally used for the map array. Therefore, this study present a determination of the optimized map structure for the rainfall-runoff analysis in Naju station considering the map size and array simultaneously which can represent the classified characterization of rainfall-runoff relationship. The result showed that the map size of 20$ imes$16 hexagonal array with 8-clustered patterns was selected as an appropriate map structure for rainfall-runoff analysis in Naju station.
 
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°­¿ì-À¯Ãâ;Àΰø½Å°æ¸Á;ÀÚ±âÁ¶Á÷È­;ÆÐÅϺзù;ÁöµµÅ©±â;Áöµµ¹è¿­;rainfall-runoff;Artificial Neural Networks (ANNs);Self-Organizing Map (SOM);pattern classification;map size;map array;
 
Çѱ¹¼öÀÚ¿øÇÐȸ³í¹®Áý / v.41, no.10, 2008³â, pp.995-1007
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1226-6280
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO200832450195774)
¾ð¾î : Çѱ¹¾î
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø
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