Citation

Obeng F, Domeh V, Khan F, Bose N, Sanli E. Safety Sci. 2022; 145: e105500.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.ssci.2021.105500

PMID

unavailable

Abstract

Fishing is considered one of the most dangerous occupations globally. Small-scale fisheries, which make up about 90% of the entire industry worldwide, are done using small boats with little onboard shelter and limited navigation and safety equipment. Small-scale fishing uses small fishing vessels such as small trawlers, which are prone to accidents, such as capsize. This paper proposes applying the Object-Oriented Bayesian Network (OOBN) to capture the risk influencing factors of the capsizing accident scenario under different operating conditions for a small fishing trawler, a sub-class of fishing vessels. The model dynamically assesses the probability of capsizing occurrence, considering the complex interaction among critical influencing parameters. The application of the proposed model is demonstrated on a small fishing trawler. To enhance the applicability of the model, uncertainty analysis was also conducted. The probability of capsize is estimated as 0.092±0.003. A study considering the most critical contributing factors was also performed to identify key risk-reducing measures. The most critical measure identified are the human elements (training and experience). The proposed model would serve as a tool for the maritime industry and governmental regulatory bodies for decision making.

Language: en

Keywords

Accident; Bayesian Network; Capsize; Likelihood; Risk control; Sensitivity; Small trawler