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Çѱ¹¼öÀÚ¿øÇÐȸ / v.38, no.8, 2005³â, pp.617-629
½Ãº¯µ¿ÀÇ µ¿Áú¼º Áõ°¡¿¡ ÀÇÇÑ ºñ´ÜÁ¶Àû ½Ã°è¿­ÀÚ·áÀÇ °æÇ⼺ ŽÁö·Â Çâ»ó
( Improved Trend Estimation of Non-monotonic Time Series Through Increased Homogeneity in Direction of Time-variation )
¿À°æµÎ;¹Ú¼ö¿¬;À̼øÃ¶;Àüº´È£;¾È¿ø½Ä; À°±º»ç°üÇб³ Åä¸ñ°øÇаú;ÇÑÁøÁ¤º¸Åë½Å GIS ±â¼úÆÀ;¼ö¿ø´ëÇб³;À°±º»ç°üÇб³ Åä¸ñ°øÇаú;¼ö¿ø´ëÇб³ Åä¸ñ°øÇаú;
 
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º» ³í¹®Àº ºñ´ÜÁ¶ÀûÀ¸·Î º¯µ¿ÇÏ´Â ½Ã°è¿­ÀڷḦ ´ÜÁ¶ÀûÀ¸·Î º¯È­ÇÏ´Â ±¸°£À¸·Î ºÐÇÒÇÏ¿© °æÇ⼺À» ºÐ¼®ÇÔÀ¸·Î½á ÀÚ·áÀÇ ½Ãº¯µ¿¿¡ ´ëÇÑ µ¿Áú¼ºÀ» Çâ»ó½ÃŰ°í ±×¿¡ µû¶ó °æÇ⼺ ºÐ¼®±â¹ýÀÇ Å½Áö·ÂÀ» Çâ»ó½Ãų ¼ö ÀÖ´Ù´Â °¡¼³À» ÀüÁ¦·Î Çϰí ÀÖ´Ù. À̸¦ °ËÅäÇϱâ À§ÇÑ ±â¹ýÀ¸·Î¼­ ½Ã°è¿­ÀÚ·áÀÇ º¯µ¿°æÇâÀ» ÆÄ¾ÇÇϱâ À§ÇÑ ÇÊÅ͸µ ¹æ¹ýÀ¸·Î LOWESS smoothingÀ» Àû¿ëÇÏ¿´°í, ½Ã°è¿­ÀÚ·áÀÇ °æÇ⼺ºÐ¼®Àº seasonal Kendall test¸¦ Àû¿ëÇÏ¿´´Ù. ÀÎÀ§ÀûÀ¸·Î ¹ß»ý½ÃŲ ½Ã°è¿­ÀÚ·á¿Í ´ëûȣÀÇ ¼ö¿Â, À¯·®, ±â¿Â, Àϻ緮 µîÀÇ ½Ã°è¿­ÀڷḦ ´ë»óÀ¸·Î °ËÅäÇÑ °á°ú ºñ´ÜÁ¶ÀûÀÎ º¯È­¸¦ º¸ÀÌ´Â ½Ã°è¿­ÀڷḦ ´ÜÁ¶ÀûÀÎ º¯È­±¸°£À¸·Î ºÐÇÒÇÏ¿© °æÇ⼺À» ºÐ¼®ÇÔÀ¸·Î½á ÀÚ·áÀÇ º¯µ¿ °æÇ⼺°ú ±â¿ï±â ÆÇÁ¤ÀÇ Á¤È®µµ¸¦ ³ôÀÏ ¼ö ÀÖ¾ú´Ù. ±×¸®°í, ÀÚ·áÀÇ ½Ãº¯µ¿¿¡ ´ëÇÑ µ¿Áú¼º Çâ»óÀº °èÀý º¯µ¿¼ºÀÇ µ¿Áú¼º¿¡ ´ëÇÑ º¯È­¸¦ º¸´Ù Á¤È®ÇÏ°Ô ºÐ¼®Çϴµ¥ µµ¿òÀ» ÁÖ´Â °ÍÀ¸·Î º¸¿´À¸¸ç À̰ÍÀº ÀÚ¿¬Çö»ó¿¡ ´ëÇÑ Àΰ£È°µ¿ÀÇ ¿µÇâÀ» °íÂûÇÒ ¼ö ÀÖ´Â ÀÚ·á·Î¼­ ¾ÕÀ¸·Î ÀÌ¿¡ ´ëÇÑ ¿¬±¸°¡ ´õ ÇÊ¿äÇÒ °ÍÀ¸·Î º¸ÀδÙ. º» ³í¹®¿¡¼­ Á¦½ÃÇÑ ¹æ¹ýÀº ½Ã°è¿­ÀÚ·áÀÇ ´ÜÁ¶ÀûÀÎ °æÇ⼺À» ºÐ¼®ÇÏ´Â ±â¹ýµé¿¡ ´ëÇØ Àû¿ë °¡´ÉÇϸç, À̸¦ ÅëÇÏ¿© ȯ°æº¯È­ÀÇ °æÇ⼺¿¡ ´ëÇÑ º¸´Ù Á¤È®ÇÑ ºÐ¼®°ú ÆÇ´ÜÀÌ °¡´ÉÇØÁú °ÍÀ¸·Î ±â´ëÇÑ´Ù.
In this paper, a hypothesis is tested that division of non-monotonic time series into monotonic parts will improve the estimation of trends through increased homogeneity in direction of time-variation using LOWESS smoothing and seasonal Kendall test. From the trend analysis of generated time series and water temperature, discharge, air temperature and solar radiation of Lake Daechung, it is shown that the hypothesis is supported by improved estimation of trends and slopes. Also, characteristics in homogeneity variation of seasonal changes seems to be more clearly manifested as homogeneity in direction of time-variation is increased. And this will help understand the effects of human intervention on natural processes and seems to warrant more in-depth study on this subject. The proposed method can be used for trend analysis to detect monotonic trends and it is expected to improve understanding of long-term changes in natural environment.
 
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°æÇ⼺ºÐ¼®;ºñ¸ð¼öÀû Åë°èÄ¡;½Ã°è¿­ÀÚ·á;Locally weighted regression smoothing;Trend analysis;Non-parametric statistics;Seasonal Kendall test;Time series;
 
Çѱ¹¼öÀÚ¿øÇÐȸ³í¹®Áý / v.38, no.8, 2005³â, pp.617-629
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1226-6280
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO200531234557671)
¾ð¾î : Çѱ¹¾î
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø
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