The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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A Study on the Distribution of Cold Water Occurrence using K-Means Clustering

K-Means Clustering을 활용한 냉수대 발생 분포에 관한 연구

  • 김범규 (한국해양과학기술원) ;
  • 윤홍주 (부경대학교 공간정보시스템공학과) ;
  • 이준호 (부경대학교 실습선 나라호)
  • Received : 2021.02.16
  • Accepted : 2021.04.17
  • Published : 2021.04.30
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Abstract

In this study, in order to analyze the spatial distribution of cold water occurred in the Southeast Sea of Korea, the K-means clustering method was used to analyze the ocean observatory buoy of Gori and Yangpo and GHTSST Level 4 from 2016 to 2018. The buoy data was used to identify the change in sea water temperature and the cold water occurrence at Gori and Yangpo in the Southeast Sea. As a result, the sea water temperature of Gori and Yangpo decreased equally at the cold water occurrence. Therefore, the reciprocal of the sea water temperature and the variance of SST were compared to see the changes of SST when the cold water occurs. When the reciprocal of the sea water temperature increases, the dispersion of SST also increases. Through this, it can be seen that there is a change in the water temperature distribution of SST in the sea when the cold water occurs. After that, K-means clustering was used to classify the cold water. After analyzing the optimal K value for clustering by using the Elbow method, it was possible to classify a region with cold water. Through this, it is estimated that the spatial distribution and diffusion range of the cold water, and it can be estimated and used in future studies to identify damage caused by the cold water and predict spatial spread.

본 연구에서는 한국 남동해역에 발생하는 냉수대의 공간적인 분포를 구분하기 위해 2016 ~ 2018년의 고리, 양포의 해양 관측 부이 수온자료와 GHTSST Level 4 재분석 해수면 온도자료를 K-means clustering 기법을 활용하여 분석하였다. 부이자료는 남동해역에서 고리와 양포 지점의 수온변화 및 냉수대 발생을 파악하기 위해 활용하였다. 그 결과 냉수대 발생 시점에 고리와 양포의 수온이 동일하게 감소하였다. 이에 냉수대 발생시 SST의 변화를 보기 위해 수온의 역수와 SST의 분산을 비교하였다. 수온이 변화하는 시점에 SST의 분산도 증가하는 것을 나타내었는데 이를 통해 냉수대 발생시 해역의 SST의 수온분포에 변화가 있다는 것을 알 수 있었다. 냉수대 발생해역을 분류하기 위해 K-means clustering을 활용하였다. Elbow 기법을 활용하여 분류를 위한 최적의 K값을 찾아낸 후 분류를 진행한 결과 연안의 차가운 해수가 존재하는 지역을 찾아낼 수 있었다. 이를 통해 냉수대 발생해역의 공간적인 분포 및 확산범위를 추정하여 향후 냉수대로 인한 피해 파악 및 공간적인 확산 예측연구에 활용할 수 있을 것이라 판단된다.

Keywords

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