Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)
ÇѱÛÁ¦¸ñ(Korean Title) |
LSTMÀ» ÀÌ¿ëÇÑ Çؾç½ÃÁ¤ ¿¹Ãø ¹æ¹ý |
¿µ¹®Á¦¸ñ(English Title) |
A Method of Estimating Sea Visibility using LSTM |
ÀúÀÚ(Author) |
Á¶¿ìÁø
°µ¿¼ö
Woojin Cho
Dongsu Kang
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 27 NO. 10 PP. 0471 ~ 0478 (2021. 10) |
Çѱ۳»¿ë (Korean Abstract) |
Çؾç½ÃÁ¤Àº Ç×°ø±â ÀÌÂø·ú, Ç×ÇØ, Çؾ緹Àú È°µ¿¿¡ Á÷Á¢ÀûÀÎ ¿µÇâÀ» ¹ÌÄ£´Ù. º» ¿¬±¸´Â ½ÃÁ¤À» ¿¹ÃøÇϱâ À§ÇØ ¸Ó½Å·¯´× ¹æ¹ýÀÎ LSTM(Long Short-Term Memory)¸¦ ÀÌ¿ëÇÑ´Ù. ±â»ó µ¥ÀÌÅÍ´Â 2012³âºÎÅÍ 2017³â±îÁö ¼ÇØ ´öÀûµµÀÇ ±º»ç ºÐ¾ß¿Í ¹Î°£ ºÐ¾ß ±â»ó µ¥ÀÌÅ͸¦ ¼öÁýÇÏ¿© Àüó¸®ÇÑ´Ù. Àüü 6³âÀÇ ±â°£ Áß 5³âÀº LSTM ¸ðµ¨¿¡ ÇнÀ½ÃÅ°°í, ³²Àº 1³âÀº °ËÁõ µ¥ÀÌÅÍ·Î »ç¿ëÇÑ´Ù. ±×¸®°í, ±âÁ¸ ¿¹Ãø¿¡¼ »ç¿ëÇÏ¿´´ø ÀÎÀÚÀÎ ½ÃÁ¤, dz¼Ó, ±â¾Ð, ½Àµµ, ±â¿Â ÀÎÀÚ¿¡ Çؼö¸é¿Âµµ¿Í ÇرâÂ÷¸¦ Ãß°¡ÇÏ¿© ¼º´É Â÷À̸¦ ºñ±³ÇÑ´Ù. ½ÇÇè °á°ú´Â ½ÇÁ¦°ª°ú ¿¹Ãø°ªÀÇ Â÷ÀÌ°¡ ¼öÄ¡ÀûÀ¸·Î ÀûÀº °ªÀ» º¸ÀδÙ. |
¿µ¹®³»¿ë (English Abstract) |
Sea visibility directly affects aircraft takeoff and landing, navigation, and marine leisure activities. In this paper, we used the Long Short-Term Memory (LSTM) of machine learning method to predict sea visibility. We collected and then preprocessed the weather data of the Deokjeok Island in the West Sea from 2012 to 2017 in the military and civilian sectors. The LSTM model was trained for five years of the entire six-year period, and the remaining one year was used as verification data. We compared performance differences by adding sea surface temperature and air-sea temperature difference to factors used in existing predictions such as visibility, wind speed, atmospheric pressure, humidity, and temperature. The experimental results showed that the difference between the actual and predicted values was numerically small. |
Å°¿öµå(Keyword) |
Çؾç½ÃÁ¤
LSTM
½ÃÁ¤ ¿µÇâÀÎÀÚ. Çؼö¸é ¿Âµµ
ÇرâÂ÷
sea visibility
LSTM
visibility influencing factors
sea surface temperature
air-sea temperature difference
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