Probabilistic modeling of domestic ferry accident causes in Kenya's Likoni ferry route using fuzzy Bayesian network

Wamugi J. W., Camliyurt G., ŞAKAR C., Park S., Park Y., AYDIN M., ...Daha Fazla

OCEAN ENGINEERING, cilt.340, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 340
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.oceaneng.2025.122388
  • Dergi Adı: OCEAN ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, ICONDA Bibliographic, INSPEC, Metadex, Civil Engineering Abstracts
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

Enhancing the reliability of Kenya's domestic ferry transportation system is crucial for mitigating safety-critical risks. This helps safeguard the lives of a daily average of 150,000 passengers and 5800 motorists who rely on ferry transport in the Mombasa channel. This study utilizes causal probabilistic modeling through a fuzzy Bayesian network approach to assess the risks associated with 18 causal factors and their 11 interdependencies. These risk factors were extracted from historical ferry accidents in the Likoni ferry route using the grounded theory approach. The developed Bayesian network model calculated a risk value indicating a 39.1 % probability of ferry accidents. Propulsion System Failure was identified as the most critical causal factor, with a fuzzy possibility score (FPS) of 0.423. Forward predictive reasoning demonstrated that implementing risk-reduction measures could lower the likelihood of ferry accidents to 21.2 %. Additionally, backward diagnostic reasoning pinpointed the two most critical causes: inadequate crew competence and propulsion system failure. The model was validated using sensitivity analysis and three-axiom validation approach, underscoring its capability to evaluate the influence of causal factors.