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Çѱ¹ÇÏõȣ¼öÇÐȸ / v.41, no.spc, 2008³â, pp.11-20
³ó¾÷¿ë Àú¼öÁöÀÇ ¼öÁú ¿¹Ãø ¸ðµ¨À» À§ÇÑ PSO(Particle Swarm Optimization) ¾Ë°í¸®ÁòÀÇ Àû¿ë
( Application of Particle Swarm Optimization(PSO) for Prediction of Water Quality in Agricultural Reservoirs of Korea )
±Ç¿ë¼ö;¹è¹ÌÁ¤;Ȳ¼øÁø;¹Ú¿µ¼®; °æÈñ´ëÇб³ »ý¹°Çаú;°æÈñ´ëÇб³ »ý¹°Çаú;°Ç±¹´ëÇб³ ȯ°æ°úÇаú;°æÈñ´ëÇб³ »ý¹°Çаú;
 
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º» ¿¬±¸¿¡¼­´Â ³ó¸²ºÎ¿Í ȯ°æºÎÀÇ Àü±¹¼öÁúÃøÁ¤¸Á ÀڷḦ ÀÌ¿ëÇÏ¿© 2002³â 9¿ù ÀüÈÄ¿¡ Á¶»çµÈ Àü±¹ 290°³ ³ó¾÷¿ë Àú¼öÁöÀÇ Chl-${alpha}$ ³óµµ¸¦ ¿¹ÃøÇÏ¿´´Ù. ¿ì¸®³ª¶ó 290°³ ³ó¾÷¿ë Àú¼öÁöÀÇ 9¿ù ÀüÈÄ ¿µ¾ç»óŸ¦ ºÐ·ùÇÑ °á°ú, ºÎ¿µ¾ç »óÅ ÀÌ»óÀ» ³ªÅ¸³»´Â Àú¼öÁö°¡ $TSI_{CHL};64.1%,;TSI_{TP};75.5%$·Î ´ëºÎºÐÀÇ Àú¼öÁö°¡ ³ôÀº ºÎ¿µ¾çÈ­ »óŸ¦ º¸¿´´Ù. ÀÌ·¸°Ô ºÐ·ùµÈ Àú¼öÁöÀÇ ¿µ¾ç »óŸ¦ ȯ°æÆ¯¼º¿¡ µû¶ó ÆÇº°ºÐ¼®À» ½Ç½ÃÇÏ¿´´Ù. ±× °á°ú Àüü ÆÇº°ÀûÁß·üÀº ¾à 60%¸¦ º¸¿´´Ù. ÆÇº°ºÐ¼®ÀÇ °á°ú¿¡ Á¤Áغм®À» ½Ç½ÃÇÑ °á°ú, °¢ ±×·ìÀº ¿µ¾ç»óÅ¿¡ µû¶ó ±¸ºÐÀÌ µÇ¾úÀ¸¸ç, COD, DO, TPµîÀÌ Áß¿äÇÑ ÀÎÀÚ·Î ³ªÅ¸³µ´Ù. ¶ÇÇÑ MLP-PSO ¸ðµ¨À» ÀÌ¿ëÇÏ¿© ºÎ¿µ¾çÈ­¿¡ µû¸¥ Àú¼öÁö ¼öÁúÀ» ¿¹ÃøÇÑ °á°ú ³ôÀº ¿¹Ãø·ÂÀ» º¸¿´À¸¸ç (r=0.831, p<0.05), ¹Î°¨µµ ºÐ¼® °á°ú COD¿Í TP°¡ »ó´ëÀûÀ¸·Î °¡Àå Áß¿äÇÑ ¿äÀÎÀ¸·Î ÀÛ¿ëÇÏ¿´À¸¸ç, °íµµ ¹× Á¦¹æ ³ôÀÌ´Â À½ÀÇ ¿µÇâÀ» ¹ÌÄ¡´Â °ÍÀ¸·Î ³ªÅ¸³µ´Ù.
In this study, we applied a Particle Swarm Optimization (PSO) algorithm to predict the changes of chlorophyll-${alpha}$ related to environmental factors in agricultural reservoirs in Korean national scale. Data were obtained from water quality monitoring networks of reservoirs operated by the Ministry of Agriculture and Forestry and the Ministry of Environment of Korea. From the database of the monitoring networks, 290 reservoirs were chosen with variables such as chlorophyll-${alpha}$ and 13 environmental factors (COD, TN, TP, Altitude, Bank height, etc.) measured in 2002. Based on Carlson's trophic status index, reservoirs were divided into five groups, and most agricultural reservoirs $(TSI_{CHL};64.1%,;TSI_{TP};75.5%)$ were in the eutrophic states. The groups were discriminated with environmental variables, showing that COD, DO, and TP were important factors to determine the trophic states. MLP-PSO (Multilayer perceptron (MLP) with PSO for the optimization) was applied for the prediction of chlorophyll-${alpha}$ with environment factors, and showed high predictability (r=0.83, p<0.001). Additionally, the sensitivity analysis of the MLP-PSO model showed that COD had the strongest positive effects on the concentration of chlorophyll-${alpha}$, and followed by TP, TN, DO, whereas altitude and bank height had negative effects on the concentration of chlorophyll-${alpha}$.
 
Ű¿öµå
agricultural reservoir;water quality;trophic status index;particle swarm optimization;discriminant analysis;
 
Çѱ¹ÇÏõȣ¼öÇÐȸÁö / v.41, no.spc, 2008³â, pp.11-20
Çѱ¹ÇÏõȣ¼öÇÐȸ
ISSN : 1976-8087
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO200814364033950)
¾ð¾î : Çѱ¹¾î
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø
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ȸ»ç¼Ò°³ ±¤°í¾È³» ÀÌ¿ë¾à°ü °³ÀÎÁ¤º¸Ãë±Þ¹æÄ§ Ã¥ÀÓÀÇ ÇѰè¿Í ¹ýÀû°íÁö À̸ÞÀÏÁÖ¼Ò ¹«´Ü¼öÁý °ÅºÎ °í°´¼¾ÅÍ
   

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