해양환경안전학회지 (Journal of the Korean Society of Marine Environment & Safety)

  • 제23권7호
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  • Pages.775-782
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  • 2017
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  • 1229-3431(pISSN)
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  • 2287-3341(eISSN)
해양환경안전학회 (The Korean Society of Marine Environment and safety)
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해양사고 신속대응을 위한 k-평균 군집화 기반 경비함정 최적배치

Optimal Arrangement of Patrol Ships based on k-Means Clustering for Quick Response of Marine Accidents

  • 유상록 (해양경찰청 완도해상교통관제센터) ;
  • 정초영 (군산대학교 해양산업.운송과학기술학부)
  • Yoo, Sang-Lok (Wando Vessel Traffic Services, Korea Coast Guard) ;
  • Jung, Cho-Young (Division of Marine Industry - Transportation Science and Technology, Kunsan National University)
  • 투고 : 2017.09.07
  • 심사 : 2017.12.28
  • 발행 : 2017.12.31
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초록

현 경비 함정의 위치는 해양사고 위치와의 접근성이 떨어져 있어 합리적이고 과학적인 기준이 아닌 주관적인 판단으로 배치되어 있다. 이에 본 연구에서는 과거 해양사고 데이터를 기반으로 정량적으로 최적의 경비 함정 배치 위치를 도출하고자 한다. 연구 해역은 포항 연안을 대상으로 하였다. 본 연구에서는 k-평균 군집화 알고리즘으로 경비 함정의 배치 위치를 도출한 후, 보로노이 다이어그램으로 각 경비 함정 간 경비 구역을 구획하였다. 연구 결과, 해양사고 1건당 경비 함정의 평균 항해 거리는 4.4해리, 평균 도착 시간은 13.2분이 개선될 수 있었다. 경비 함정을 유동적으로 배치 수를 달리해야 할 경우 본 연구에서 적용한 기법을 활용하여 최적 배치가 가능하며, 신속한 구조 지원 체계가 더욱 확보될 것으로 판단된다.

The position of existing patrol ships has been decided according to subjective judgments, not purely by any reasonable or scientific criteria, because of a lack of access to marine accident positions. In this study, the optimal location of patrol ships is quantitatively determined based on historical marine accident data. The study area used included the coastal sea of Pohang in South Korea. In this study, a k-means clustering algorithm was used to derive the location of patrol ships, and then a Voronoi diagram was used to divide the region around each patrol ship. As a result, the average navigation distance for patrol ships was improved by 4.4 nautical miles, and the average arrival time was improved by 13.2 minutes per marine accident. Moreover, if the locations of patrol ships need to be changed flexibly, it will be possible to optimally arrange limited resources using the technique developed in this study to ensure a fast rescue.

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피인용 문헌

  1. Optimal Rescue Ship Locations Using Image Processing and Clustering vol.11, pp.1, 2017, https://doi.org/10.3390/sym11010032