A Hybrid Fault Tree–Fuzzy Bayesian Network Framework for Ferry Accident Risk Assessment in Tanzania: Lessons from the 2018 MV Nyerere Disaster

Domestic ferry operations in Tanzania present a distinctive safety challenge, marked by a history of severe accidents, fragmented causal evidence, and scarce operating-exposure data, which together render conventional frequency-based risk assessment methods impractical. This study develops and applies a hybrid Fault Tree Analysis–Fuzzy Bayesian Network (FTA–FBN) framework to assess the risk of serious ferry accidents in Tanzanian domestic operations. A comprehensive review of documentary evidence from 1996 to 2025 was conducted to identify and harmonize causal factors and to construct a compact fault tree. This structure was subsequently validated by fifteen maritime experts, who also provided linguistic probability judgments. Expert competence was quantified using four established criteria, while linguistic assessments were represented as triangular fuzzy numbers, then competence-weighted, aggregated, and defuzzified to derive five root priors and 32 conditional probability conditions. The resulting 14-node Bayesian network was implemented in GeNIe and subjected to baseline, forward, backward, sensitivity, and behavioral validation analyses. The model yielded a baseline Serious Ferry Accident probability of 72.40%, interpreted as an expert-informed measure of model risk propensity rather than an observed voyage-level accident frequency. Forward analysis revealed that setting improper loading to “No” yielded the largest individual root-level risk reduction, while setting all five root risks to “No” reduced the top-event probability to 54.83%. Sensitivity analyses consistently emphasized the stability/capsize pathway and loading-related parameters as dominant contributors. In a retrospective consistency check using the MV Nyerere case, when overloading and capsize/sinking were used as evidence, the posterior probability of Loss of Stability rose to 96.8%. The present study provides a transparent and updateable approach for risk diagnosis and intervention prioritization within a data-constrained domestic ferry system.