Analyzing operational risk for small fishing vessels considering crew effectiveness

Obeng, Francis; Domeh, Vindex; Khan, Faisal; Bose, Neil; Sanli, Elizabeth

doi:10.1016/j.oceaneng.2021.110512

Cited by 15 publications

(9 citation statements)

References 30 publications

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“…Firstly, due to complex operational environments of AUVs, multiple risk factors could interact and cause vehicle loss. While BN contains a clear topological structure to present causal relationships among complex risk variables, which facilities risk identification especially for a multi-variable system (Obeng et al, 2022). Secondly, BN is a probabilistic risk assessment tool, and using the conditional probability theory could enhance the accuracy of risk prediction.…”

Section: Methodsmentioning

confidence: 99%

Risk-based path planning for autonomous underwater vehicles in an oil spill environment

et al. 2022

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Section: Methodsmentioning

confidence: 99%

Risk-based path planning for autonomous underwater vehicles in an oil spill environment

et al. 2022

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“…Studies by Jin et al [8,9], Davis et al [17], Heij and Knapp [18], Weng et al [19], and others have identified correlations between adverse weather conditions, seasons, and the occurrence and severity of accidents. By employing advanced machine learning methods, Rezaee et al [20], Liu et al [21], and Wang et al [22] further clarified the relationship between weather conditions and accident severity. Özaydın et al [23] used BN and association rule mining (ARM) methods to demonstrate the impact of adverse weather and sea conditions on fishing vessel accidents.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Risk Influential Factors for Fishing Vessel Accidents Using Claims Data from Fishery Mutual Insurance Association

Wang,

Du,

Feng

et al. 2023

Sustainability

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This research aims to identify and analyze the significant risk factors contributing to accidents involving fishing vessels, a crucial step towards enhancing safety and promoting sustainable practices in the fishing industry. Using a data-driven Bayesian network (BN) model that incorporates feature selection through the random forest (RF) method, we explore these key factors and their interconnected relationships. A review of past academic studies and accident investigation reports from the Fishery Mutual Insurance Association (FMIA) revealed 17 such factors. We then used the random forest model to rank these factors by importance, selecting 11 critical ones to build the Bayesian network model. The data-driven Bayesian network (BN) model is further utilized to delve deeper into the central factors influencing fishing vessel accidents. Upon validation, the study results show that incorporating the random forest feature selection method enhances the simplicity, reliability, and precision of the BN model. This finding is supported by a thorough performance evaluation and scenario analysis.

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“…As a result of the verification using data from 857 marine accidents, weather and wave height were perceived as extremely dangerous factors among fishing boat crews and insignificant risk factors among recreational boats. Obeng et al [29] proposed a new human factor analysis model to analyze small fishing boat operations, and this model was combined with a BN and tested, focusing on small fishing boats operating in the Atlantic Canada region. They estimated that the main causes of accidents were the operator's actions, the natural and technological environment, unsafe management of operations, and factors associated with the vessel itself.…”

Section: Introductionmentioning

confidence: 99%

Human Reliability Analysis for Fishing Vessels in Korea Using Cognitive Reliability and Error Analysis Method (CREAM)

Lee,

Kim,

Koo

et al. 2024

Sustainability

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In this paper, we introduce a model designed to predict human error probability (HEP) in the context of fishing boat operations utilizing the cognitive reliability and error analysis method (CREAM). We conducted an analysis of potential accidents on fishing boats and calculated the cognitive failure probability (CFP) for each identified accident. The common performance conditions (CPCs) from the original CREAM were adapted to better reflect the conditions on fishing boats, with the adapted CPCs’ validity confirmed through expert consultations. To apply CREAM, data were gathered via a survey of fishermen, with the uncertainty in the collected data addressed through the application of fuzzy set theory (FST). We then established a Bayesian network (BN) model to elucidate the relationship between the fuzzy data and HEP, utilizing a weighted sum algorithm to determine conditional probabilities within the BN. Both basic and extended versions of CREAM were applied to analyze the most common accidents among fishermen, calculating the CFP for each type of accident. According to our analysis, the poorer the dynamic CPC, the higher the probability that a fall accident will occur inside the boat due to human error, necessitating a countermeasure. The paper proposes safety enhancements for small fishing boats and illustrates the increased precision of human reliability analysis (HRA) models in forecasting human error by incorporating quantitative methods. It calls for further data collection and refinement of the model for more accurate operational risk assessments.

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Analyzing operational risk for small fishing vessels considering crew effectiveness

Cited by 15 publications

References 30 publications

Risk-based path planning for autonomous underwater vehicles in an oil spill environment

Risk-based path planning for autonomous underwater vehicles in an oil spill environment

Identification of Risk Influential Factors for Fishing Vessel Accidents Using Claims Data from Fishery Mutual Insurance Association

Human Reliability Analysis for Fishing Vessels in Korea Using Cognitive Reliability and Error Analysis Method (CREAM)

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