Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)
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ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
Conformity Assessment of Machine Learning Algorithm for Particulate Matter Prediction |
ÀúÀÚ(Author) |
Á¶°æ¿ì
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Kyoung-woo Cho
Yong-jin Jung
Chul-gyu Kang
Chang-heon Oh
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¿ø¹®¼ö·Ïó(Citation) |
VOL 23 NO. 01 PP. 0020 ~ 0026 (2019. 01) |
Çѱ۳»¿ë (Korean Abstract) |
¹Ì¼¼¸ÕÁöÀÇ ÀÎü ¿µÇâÀ¸·Î ÀÎÇØ ±âÁ¸ ´ë±â ȯ°æ ¸ð´ÏÅ͸µ ³×Æ®¿öÅ©¿¡¼ ÃøÁ¤µÈ °ú°Å µ¥ÀÌÅ͸¦ È°¿ëÇÏ¿© ¹Ì¼¼¸ÕÁö¸¦ ¿¹ÃøÇÏ·Á´Â ´Ù¾çÇÑ ¿¬±¸°¡ ÁøÇàµÇ°í ÀÖ´Ù. ÇÏÁö¸¸ ±âÁ¸ ¼³°èµÈ ¿¹Ãø ¸ðµ¨ÀÇ ÃøÁ¤ ȯ°æ, ¼¼ºÎ Á¶°ÇÀ» Á¤È®È÷ ¼³Á¤Çϱ⠾î·Á¿ì¸ç, ÃøÁ¤µÈ ±â»ó µ¥ÀÌÅÍÀÇ ´©¶ô°ú °°Àº ¹®Á¦·Î ±âÁ¸ ¿¬±¸ °á°ú¿¡ ±â¹Ý ÇÑ »õ·Î¿î ¿¹Ãø ¸ðµ¨ÀÇ ¼³°è°¡ ÇÊ¿äÇÏ´Ù. º» ³í¹®¿¡¼´Â ¹Ì¼¼¸ÕÁö ¿¹ÃøÀ» À§ÇÑ ¼±Çà ¿¬±¸·Î¼ ´Ù¼öÀÇ ¿¬±¸¿¡¼ »ç¿ëµÈ ±â°è ÇнÀ ¾Ë°í¸®ÁòÀÎ ´ÙÁß ¼±Çü ȸ±Í¿Í Àΰø ½Å°æ¸ÁÀ» ÅëÇØ ¿¹Ãø ¸ðµ¨À» ¼³°èÇÏ¿© ¹Ì¼¼¸ÕÁö ¿¹ÃøÀ» À§ÇÑ ¾Ë°í¸®ÁòÀÇ ÀûÇÕ¼ºÀ» Æò°¡ÇÏ¿´´Ù. RMSE¸¦ ÅëÇÑ ¿¹Ãø ¼º´É ºñ±³ °á°ú, MLR ¸ðµ¨ÀÇ °æ¿ì 18.13, MLP ¸ðµ¨ÀÇ °æ¿ì 14.31ÀÇ °ªÀ» º¸¿© ¹Ì¼¼¸ÕÁö ³óµµ¸¦ ¿¹ÃøÇÔ¿¡ ÀÖ¾î Àΰø ½Å°æ¸Á ¸ðµ¨ÀÌ ¿¹Ãø¿¡ ´õ ÀûÇÕÇÔÀ» º¸¿´´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Due to the human influence of particulate matter, various studies are being conducted to predict it using past data measured in the atmospheric environment monitoring network. However, it is difficult to precisely set the measurement environment and detailed conditions of the previously designed predictive model, and it is necessary to design a new predictive model based on the existing research results because of the problems such as the missing of the weather data. In this paper, as a previous study for particulate matter prediction, the conformity of the algorithm for particulate matter prediction was evaluated by designing the prediction model through the multiple linear regression and the artificial neural network, which are machine learning algorithms. As a result of the prediction performance comparison through RMSE, 18.13 for the MLR model and 14.31 for the MLP model, and the artificial neural network model was more conformable for predicting the particulate matter concentration.
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Å°¿öµå(Keyword) |
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±â°è ÇнÀ
µö ·¯´×
Àΰø ½Å°æ¸Á
Particulate matter
Prediction
Machine learning
Deep learning
Artificial neural network
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