Journal of the Korean Society of Fisheries and Ocean Technology (수산해양기술연구)

The Korean Society of Fisheries and Ocean Technology (한국수산해양기술학회)
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Study of estimated model of drift through real ship

실선에 의한 표류 예측모델에 관한 연구

  • Chang-Heon LEE (College of Ocean Sciences, Jeju National University) ;
  • Kwang-Il KIM (College of Ocean Sciences, Jeju National University) ;
  • Sang-Lok YOO (Research Institute, Future Ocean Information Technology, Inc.) ;
  • Min-Son KIM (Marine Production System Major, Kunsan National University) ;
  • Seung-Hun HAN (Mechanical System Engineering, Gyeongsang National University)
  • 이창헌 (제주대학교 해양과학대학) ;
  • 김광일 (제주대학교 해양과학대학) ;
  • 유상록 ((주)미래해양정보기술 기업부설연구소) ;
  • 김민선 (국립군산대학교 해양생산시스템전공) ;
  • 한승훈 (경상국립대학교 기계시스템공학과)
  • Received : 2024.01.08
  • Accepted : 2024.01.30
  • Published : 2024.02.28
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Abstract

In order to present a predictive drift model, Jeju National University's training ship was tested for about 11 hours and 40 minutes, and 81 samples that selected one of the entire samples at ten-minute intervals were subjected to regression analysis after verifying outliers and influence points. In the outlier and influence point analysis, although there is a part where the wind direction exceeds 1 in the DFBETAS (difference in Betas) value, the CV (cumulative variable) value is 6%, close to 1. Therefore, it was judged that there would be no problem in conducting multiple regression analyses on samples. The standard regression coefficient showed how much current and wind affect the dependent variable. It showed that current speed and direction were the most important variables for drift speed and direction, with values of 47.1% and 58.1%, respectively. The analysis showed that the statistical values indicated the fit of the model at the significance level of 0.05 for multiple regression analysis. The multiple correlation coefficients indicating the degree of influence on the dependent variable were 83.2% and 89.0%, respectively. The determination of coefficients were 69.3% and 79.3%, and the adjusted determination of coefficients were 67.6% and 78.3%, respectively. In this study, a more quantitative prediction model will be presented because it is performed after identifying outliers and influence points of sample data before multiple regression analysis. Therefore, many studies will be active in the future by combining them.

Keywords

Acknowledgement

본 연구(결과물)는 2023년도 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 3단계 산학연협력 선도대학 육성사업(LINC 3.0) (1345370630) 및 2023년도 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 지자체-대학 협력기반 지역혁신 사업의 결과입니다(2023RIS-009).

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