@article{ref1,
title="Capsizing accident scenario model for small fishing trawler",
journal="Safety science",
year="2022",
author="Obeng, Francis and Domeh, Vindex and Khan, Faisal and Bose, Neil and Sanli, Elizabeth",
volume="145",
number="",
pages="e105500-e105500",
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.<p /> <p>Language: en</p>",
language="en",
issn="0925-7535",
doi="10.1016/j.ssci.2021.105500",
url="http://dx.doi.org/10.1016/j.ssci.2021.105500"
}