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Çѱ¹¼öÀÚ¿øÇÐȸ / v.29, no.6, 1996³â, pp.179-188
°­¼ö°è¿­ÀÇ »óźзù¿¡ ÀÇÇÑ Markov ¿¬¼â ¸ðÀǹ߻ý ¸ðÇü
( Markov Chain Model for Synthetic Generation by Classification of Daily Precipitation Amount into Multi-State )
±èÁÖȯ;¹ÚÂù¿µ;°­°ü¿ø; Çѱ¹¼öÀÚ¿ø°ø»ç ¼öÀÚ¿ø¿¬±¸¼Ò;ÀÎÇϰø¾÷Àü¹®´ëÇÐ Åä¸ñ°ú;ÀÎÇÏ´ëÇб³ Åä¸ñ°øÇаú;
 
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¼öÀÚ¿øÀÇ ÁÖ°ø±Þ¿øÀÎ °­¼ö´Â Çö»óÀÇ ¹ß»ý¿©ºÎ¿¡ µû¶ó °ÇÁ¶Àϰú ½ÀÀ±ÀÏÀÌ ±³´ë·Î ¹Ýº¹µÇ´Â °úÁ¤À¸·Î ±¸¼ºµÇ¾î ÀÖÀ¸¸ç, ƯÈ÷, Àϰ­¼ö°è¿­ÀÇ ½ÀÀ±ÀÏ¿¡ ¹ß»ýµÇ´Â °­¼ö·®ÀÇ Å©±â´Â ¸Å¿ì ´Ù¾çÇÑ ÇüŸ¦ Áö´Ï°í ÀÖ¾î ÀÌ °úÁ¤À» ¸ðÇüÈ­ Çϴµ¥´Â º¹ÀâÇÑ È®·ü°úÁ¤ÀÌ ¼ö¹ÝµÈ´Ù. º» ¿¬±¸¿¡¼­´Â Àϰ­¼ö°è¿­ÀÇ ¹ß»ý°úÁ¤À» °ÇÁ¶ÀÏ, ½ÀÀ±ÀÏ·Î ±¸ºÐÇÏ°í ½ÀÀ±ÀÏÀÇ °­¼ö·®À» »óź°·Î ºÐ·ùÇÏ¿© °¢ »óź° õÀÌÈ®·üÀ» °è»êÇÔÀ¸·Î½á À̸¦ Àå·¡¿¡ ¹ß»ý °¡´ÉÇÑ °­¼ö»ç»óÀÇ ¸ðÀǹ߻ý¿¡ ÀÌ¿ëÇÏ¿´´Ù. º» ¸ðÇüÀº ¼ö¹®»ç»óÀÇ ¹ß»ý°ú ºñ¹ß»ý¸¸À» ±¸ºÐÇÏ´ø 2-state Markov ¿¬¼â¸ðÇü¿¡ °­¼öÀÇ ¹ß»ý½Ã °­¼ö·®ÀÇ Å©±â¿¡ µû¶ó »óŸ¦ ¿©·¯ °³·Î ±¸ºÐÇÏ¿© °­¼ö·®À» ÃßÁ¤ÇÒ ¼ö ÀÖµµ·Ï ¼öÁ¤ÇÑ °ÍÀ¸·Î °£Çæ ¼ö¹®»ç»óÀÎ Àϰ­¼ö°è¿­ÀÇ ±¸¼º¼ººÐÀÎ °ÇÁ¶Àϰú ½ÀÀ±ÀÏ, °ÇÁ¶, ½ÀÀ± Áö¼Ó±â°£ ¹× ½ÀÀ±ÀÏÀÇ °­¼ö·®À» Markov ¿¬¼â¿¡ ÀÇÇØ µ¿½Ã¿¡ ¹ß»ýÀÖµµ·Ï ÇÑ °ÍÀÌ¸ç ´Ù¸¥ ¸ðÇü¿¡ ºñÇØ »ç¿ëÀÌ ºñ±³Àû ¿ëÀÌÇÏ´Ù. º» ¿¬±¸¿¡¼­ Á¦¾ÈÇÑ multi-state Markov ¿¬¼â¸ðÇüÀÇ Àû¿ë °¡´É¼ºÀ» °ËÅäÇϱâ À§ÇÏ¿© ºñ±³Àû Àå±â°£ÀÇ ÀڷḦ º¸À¯Çϰí ÀÖ´Â °üÃø¼ÒÀÇ °­¼öÀڷḦ ÀÌ¿ëÇÏ¿´À¸¸ç ±× °á°ú¸¦ °­¼ö·®, °ÇÁ¶, ½ÀÀ±Àϼö ¹× °ÇÁ¶, ½ÀÀ±°è¼Ó±â°£ÀÇ ºÐÆ÷¸¦ ½ÇÁ¦ÀÚ·á¿Í ºñ±³ÇÏ¿© ¸ðÇüÀÇ ÀûÇÕµµ¸¦ Æò°¡ÇÏ¿´´Ù. À̸¦ Åä´ë·Î È«¼ö ¹× Çѹ߱ⰣÀÇ ÃßÁ¤°ú ¸ðÀǹ߻ý¿¡ ÀÇÇÑ ÀÚ·á È®ÀåÀ¸·Î ÁßÀå±â ¼öÀÚ¿ø °èȹ ¹× ¿î¿µ¿¡ È¿À²ÀûÀ¸·Î ÀÌ¿ëµÉ ¼ö ÀÖÀ» °ÍÀ¸·Î ÆÇ´ÜµÈ´Ù.
The chronical sequences of daily precipitation are of great practical importance in the planning and operational processes of water resources system. A sequence of days with alternate dry day and wet day can be generated by two state Markov chain model that establish the subsequent daily state as wet or dry by previously calculated vconditional probabilities depending on the state of previous day. In this study, a synthetic generation model for obtaining the daily precipitation series is presented by classifying the precipitation amount in wet days into multi-states. To apply multi-state Markov chain model, the daily precipitation amounts for wet day are rearranged by grouping into thirty states with intervals for each state. Conditional probabilities as transition probability matrix are estimated from the computational scheme for stepping from the precipitation on one day to that on the following day. Statistical comparisons were made between the historical and synthesized chracteristics of daily precipitation series. From the results, it is shown that the proposed method is available to generate and simulate the daily precipitation series with fair accuracy and conserve the general statistical properties of historical precipitation series.
 
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Çѱ¹¼öÀÚ¿øÇÐȸÁö / v.29, no.6, 1996³â, pp.179-188
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1738-9488
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO199611920098125)
¾ð¾î : ¿µ¾î
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø
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