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

Fang Wang, Weijie Du, Hongxiang Feng, Yun Ye, Manel Grifoll, Guiyun Liu () and Pengjun Zheng ()
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Fang Wang: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Weijie Du: Ningbo Pilot Station, Ningbo 315040, China
Hongxiang Feng: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Yun Ye: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Manel Grifoll: Barcelona School of Nautical Studies, Universitat Politècnica de Catalunya—BarcelonaTech, 08003 Barcelona, Spain
Guiyun Liu: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Pengjun Zheng: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China

Sustainability, 2023, vol. 15, issue 18, 1-24

Abstract: 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.

Keywords: fishing vessel; accident analysis; random forest; Bayesian network; feature selection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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