Skoči na glavni sadržaj

Prethodno priopćenje

https://doi.org/10.31217/p.37.1.11

A Port Entry Risk Assessment Model Based on Bayesian Networks and Elements of the e-Navigation Concept

Mario Musulin orcid id orcid.org/0000-0002-6638-8285 ; Hrvatsko vojno učilište "Dr. Franjo Tuđman", Zagreb, Hrvatska
Grgo Kero ; Hrvatsko vojno učilište "Dr. Franjo Tuđman", Zagreb, Hrvatska
Hrvoje Nenad Musulin ; Sveučilište u Rijeci, Pomorski fakultet, Rijeka, Hrvatska
David Brčić ; Sveučilište u Rijeci, Pomorski fakultet, Rijeka, Hrvatska


Puni tekst: engleski pdf 4.131 Kb

str. 140-150

preuzimanja: 319

citiraj


Sažetak

Most maritime accidents are caused by human error. To reduce these errors, increase safety, protect the marine environment, reduce the administrative burden, and optimize maritime trade, the International Maritime Organization (IMO) decided to implement a concept called e-Navigation. The term e-Navigation refers to the integration of the most modern information and communication systems, both on board and ashore. The research scope of this work is the safety of navigation of the ship during the port entrance. Particularly in this case, the elements that affect the safety of the ship during port entrance are analyzed in a case study of New York port. An adaptive model based on the Bayesian Belief Network (BBN) was created, which evaluates the decision-making elements and risk assessment for port entry. The model is adaptable in the context of the different port requirements. With the presented model, all entities (ships, shipping companies, and service providers) have insight into the estimated decision to enter the port based on the given elements. Further research needs to be continued on the issue of comparing the proposed model with similar models, and how much reliance on the model burdens or facilitates the Master’s decision.

Ključne riječi

e-Navigation; Safety at Sea; Bayesian Belief Network; Port Entering

Hrčak ID:

304311

URI

https://hrcak.srce.hr/304311

Datum izdavanja:

29.6.2023.

Posjeta: 791 *