Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
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Estimation of Environmental Costs Based on Size of Oil Tanker Involved in Accident using Neural Network

신경망을 이용한 유조선 기름 유출사고에 따른 환경비용 추정에 관한 연구

  • Shin, Sung-Chul (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Bae, Jeong-Hoon (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Kim, Hyun-Soo (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Kim, Seong-Hoon (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Kim, Soo-Young (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Lee, Jong-Kap (Maritime & Ocean Engineering Research Institute, KORDI)
  • 신성철 (부산대학교 조선해양공학과) ;
  • 배정훈 (부산대학교 조선해양공학과) ;
  • 김현수 (부산대학교 조선해양공학과) ;
  • 김성훈 (부산대학교 조선해양공학과) ;
  • 김수영 (부산대학교 조선해양공학과) ;
  • 이종갑 (한국해양연구원)
  • Received : 2012.01.25
  • Accepted : 2012.02.13
  • Published : 2012.02.29
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Abstract

The accident risks in the marine environment are increasing because of the tendency to build faster and larger ships. To secure ship safety, risk-based ship design (RBSD) was recently suggested based on a formal safety assessment (FSA). In the process of RBSD, a ship designer decides which risk reduction option is most cost-effective in the design stage using a cost-benefit analysis (CBA). There are three dimensions of risk in this CBA: fatality, environment, and asset. In this paper, we present an approach to estimate the environmental costs based on the size of an oil tanker involved in an accident using a neural network. An appropriate neural network model is suggested for the estimation,and the neural network is trained using IOPCF data. Finally,the learned neural network is compared with the cost regression equation by IMO MEPC 62/WP.13 (2011).

Keywords

References

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Cited by

  1. Practical Application of Neural Networks for Prediction of Ship's Performance Factors vol.29, pp.2, 2015, https://doi.org/10.5574/KSOE.2015.29.2.111