## 3 BBN Model Structuring and Verification for a Grounding Accident
### BBN Model Verification  
According to the authors [11; 41], the model should be sufficiently plausible and serve as a basis for future risk analysis. Sensitivity analysis is a technique used to verify parameters of a Bayesian network [42]. Sensitivity analysis verifies the effect of small changes of the numerical values of network variables on the posterior probabilities of observed risk factor causal variables and the effect variable. Highly sensitive causal variables will have a significant impact on the effect variable. Additionally, evaluation of the sensitivity of the effect variable may provide information on the strength of the influence of causal variables. When the sensitivity values are different from those expected, then the sensitivity analysis provides identification of the parameters, which requires correction and calibration. This is often a repetitive process, which is terminated when the expected results are observed. Thus, the model verification process evaluates how well the model specifies the system it represents [23; 36]. Verification tests cannot provide the accuracy of the model, but rather show whether the model is a credible representation of the modelled accident.  
Figure 4: Quantitative grounding model for passenger non-liner small vessels.  
The algorithm for sensitivity analysis described in [42], and implemented in GeNie [5], is used for verification of the BBN model. After selecting specific nodes called \"target nodes\", the algorithm calculates the complete posterior probability distribution derivative sets of selected nodes over all numerical parameters of the Bayesian network [5; 42]. The sensitivity testing results are given for \"grounding\" and \"loss of control\" variables, in Figures 5 and 6, respectively.  
Figure 5: Sensitivity analysis for the grounding model when targeting “grounding”.  
Figure 6: Sensitivity analysis for the grounding model when targeting “loss of control”.  
By targeting \"grounding\", the sensitivity is most prominent to \"navigation in shallow waters\" and \"special caution area\", followed by \"human error\", \"off course\", \"navigation error\", and \"loss of control\", as shown in Figure 7.  
It can be observed that \"grounding\" is sensitive to variables that refer to the group of human factors: \"familiarization\", \"stress level\", \"tired\", \"incapacitated\", and \"safety culture\". High uncertainty is also observed for external variables related to the hydro-meteorological impact, such as \"sea state\" and \"wind force\", which is in line with the reasoning that the influence of the sea state and wind can be significant for the small sailing cruisers observed in this study.  
Targeting \"loss of control\" provided the highest sensitivity to \"human error\", \"personal condition\", \"incapacitated\", and \"safety culture\" as shown in Figure 8. It can be observed that the highest sensitivity of \"loss of control\" is shown to the group of human factor variables, which corresponds to the expected results. Significant sensitivity is also shown by the variable \"maintenance\" and the variables related to technical errors.  
Figure 8: Sensitivity analysis for the grounding model when targeting “loss of control”.  
Figure 7: Sensitivity of “grounding” to causal variables of the model (w/o “navigation in shallow waters” and “special caution area”).  
The obtained results ranked the influential variables in an order that is in accordance with the theoretical knowledge on the causes of grounding accidents, which verifies that the model is sufficiently plausible for further risk analysis.