Pregled bibliografske jedinice broj: 1159586
The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment
The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment // Applied sciences (Basel), 11 (2021), 22; 10866, 20 doi:10.3390/app112210866 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1159586 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment
Autori
Vojković, Lea ; Kuzmanić Skelin, Ana ; Mohović, Đani ; Zec, Damir ;
Izvornik
Applied sciences (Basel) (2076-3417) 11
(2021), 22;
10866, 20
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Bayesian belief network ; Bayesian network validation ; risk factor assessment ; Bayesian inference ; ship grounding model ;
Sažetak
An integrative approach to maritime accident risk factor assessment in accordance with formal safety assessment is proposed, which exploits the multifaceted capabilities of Bayesian networks (BNs) by consolidation of modelling, verification, and validation. The methodology for probabilistic modelling with BNs is well known and its application to risk assessment is based on the model verified though sensitivity analysis only, while validation of the model is often omitted due to a lack of established evaluation measures applicable to scarce real-world data. For this reason, in this work, the modified Lyapunov divergence measure is proposed as a novel quantitative assessor that can be efficiently exploited on an individual accident scenario for contributing causal factor identification, and thus can serve as the measure for validation of the developed expert elicited BN. The proposed framework and its approach are showcased for maritime grounding of small passenger ships in the Adriatic, with the complete grounding model disclosed, quantitative validation performed, and its utilization for causal factor identification and risk factor ranking presented. The data from two real-world grounding cases demonstrate the explanatory capabilities of the developed approach.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Tehnologija prometa i transport, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split,
Pomorski fakultet, Rijeka,
Pomorski fakultet, Split
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- Social Science Citation Index (SSCI)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus