# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 3 Model Established for NSR Resilience Measurement
### Resilience Measurement Using the Fuzzy Bayesian Network
#### 3.4.2 Calculation of the Prior Probabilities of the Nodes without Parent Nodes  
Based on the qualitative analysis discussed in Sections 3.2 and 3.3, there are a total of 30 parent nodes included in the established Bayesian network illustrated in Figure 5. The prior probabilities of these parent nodes can be calculated according to the calculation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **Age** & **Occupation** & **Educational Level** & **Certificate Rank** & **Job Tenure** \\ \hline Expert 4 (E4) & 41 & Associate professor & Masters of marine engineering & Chief engineer & He has been working on ships since 2001, beginning as a cadet and eventually becoming a chief engineer; currently, he is an associate professor focusing on risk assessments of Arctic transport. \\ \hline Expert 5 (E5) & 43 & Safety manager & Masters of navigation technology & Chief officer & He has been working on board ships since 2001, eventually becoming a chief officer; he is familiar with marine operation and equipment management for ships transiting Arctic waters. \\ \hline Expert 5 (E5) & 43 & Safety manager & Masters of navigation technology & Chief officer & He has been working on board ships since 2001, eventually becoming a chief officer; he is familiar with marine operation and equipment management for ships transiting Arctic waters. \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indicator** & **Classification** & **Score** & **Indicator** & **Classification** & **Score** \\ \hline Professional & Senior academic & 5 & & 6–9 & 2 \\  & Junior academic & 4 & & \(\leq\)5 & 1 \\ \cline{3-6} position & Engineer & 3 & & Ph.D. & 5 \\  & Technician & 2 & & Masters & 4 \\  & Worker & 1 & Education level & B.S. or B.E. & 3 \\ \hline Age & \(\geq\)50 & 4 & & Junior college & 2 \\  & 40–49 & 3 & & School level & 1 \\  & 30–39 & 2 & & Senior Cap. or C/E & 5 \\  & \(\leq\)30 & 1 & & Cap. or C/E & 4 \\ \hline Experience (1) & \(\geq\)30 years & 5 & & C/O or 2/E & 3 \\  & 20–29 & 4 & & Operational Officer/engineer & 2 \\  & 10–19 & 3 & & ratings & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Scores of indicators for expert evaluation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Expert** & **Position** & **Experience** & **Education** & **Age** & **Certificate** & **Weight** \\ \hline Expert 1 & Senior seafarer & 30 & Bachelors & 53 & Senior Captain & 0.189 \\ Expert 2 & Senior seafarer & 27 & Bachelors & 50 & 2 nd Officer & 0.121 \\ Expert 3 & Professor & 18 & Ph.D. & 48 & Senior Captain & 0.300 \\ Expert 4 & Associate professor & 22 & Masters & 41 & Chief engineer & 0.206 \\ Expert 5 & Safety manager & 8 & Masters & 43 & Chief officer & 0.184 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Evaluation results of expert capabilities.  
procedures mentioned in Section 2.2.1. The experts involved in this process are considered independently during decision making. The results of the aggregation and defuzzification of expert judgements are presented in Table 10.