2026-04-17T04:12:51Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4051582026-01-14T07:48:56Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Wang, Fang author Du, Weijie author Feng, Hongxiang author Ye, Yun author Grifoll Colls, Manel author Liu, Guiyun author Zheng, Pengjun author 2023-09-07 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. Postprint (published version) Àrees temàtiques de la UPC::Nàutica::Seguretat marítima::Accidents marítims Àrees temàtiques de la UPC::Enginyeria agroalimentària::Pesca::Pesca marina Fishing boats--Accidents Fishers--Accidents Employers’ liability insurance Fishing vessel Accident analysis Random forest Bayesian network Feature selection Embarcacions de pesca--Accidents Pescadors--Accidents Assegurances d'accidents de treball Identification of risk influential factors for fishing vessel accidents using claims data from fishery mutual insurance association