# Assessing Fatality Risks in Maritime Accidents: The Influence of Key Contributing Factors
## 6 Discussion  
In our paper, we provide new insights into the contributing factors of fatality risks in maritime accidents, with a special focus on alcohol impairment. The findings emphasise  
Figure 9: Predictor importance in CHAID model with interactions. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Actual**} & \multicolumn{2}{c}{**Predicted**} & \multirow{2}{*}{**Total**} \\ \cline{2-3} \cline{5-5}  & **0** & **1** & \\ \hline
0 & 28 & 2 & 30 \\
1 & 3 & 26 & 29 \\ Total & 31 & 28 & 59 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification table and evaluation metrics of CHAID model with interactions. the importance of understanding how multiple variables interact to create risky situations. While underscoring the effects of alcohol on cognitive functions and physical abilities, our paper enhances this knowledge using a predictive model that quantifies the impact of alcohol in combination with meteorological conditions and time of day. This model not only confirms the significant effect of alcohol on fatality risks but also reveals how these risks are amplified when alcohol consumption coincides with adverse weather or nighttime operations. This interactive effect, which has not been explored in previous studies, fills a critical gap by providing a more nuanced understanding of the factors contributing to maritime fatalities. The findings highlighted the significance of the role of alcohol in fatal maritime accidents by using logistic regression. The analysis showed that alcohol consumption is a major predictor of fatality. Each additional unit of BAC increased the probability of a fatality by approximately 8.78 times. However, the results also demonstrated that alcohol does not act in isolation. Adverse weather conditions, often affecting visibility, significantly increase the likelihood of fatal accidents, especially when combined with alcohol consumption. Poor visibility can severely affect safe navigation and the crew's ability to respond to hazards. Nighttime conditions make this even worse by reducing vision further, as there is no natural light and crews must rely on artificial lighting. Although nighttime conditions alone did not significantly increase fatality risk, when coupled with high BAC levels and poor weather, the risk increased. The use of the CHAID tree model illustrated how this combination of high BAC, nighttime, and specific weather conditions create a key predictor of fatal accidents. Existing maritime regulations need to adapt to the broader conditions in which accidents happen. Current rules should be improved with evidence-based changes, like stricter alcohol testing during high-risk situations, mandatory sobriety checks before night voyages, and more monitoring during bad weather. Additionally, our findings suggest that safety protocols and training for maritime staff should be adjusted to address the higher risks linked to these conditions. Our paper also identifies several gaps in the earlier research, particularly the failure to investigate the combined effects of alcohol, weather, and time of day. The majority of the previous works focused on these variables in isolation, leaving the complex interplay between them unexplored. By integrating these factors into our predictive model, we provide a more comprehensive understanding of when fatalities are most likely to occur. For example, even moderate levels of alcohol, when compounded by poor weather or nighttime conditions, can lead to fatal outcomes. This supports the need for integrated risk management strategies that address multiple interacting factors rather than isolated risks. Previous research on the effects of alcohol on maritime safety underscores the significant role it plays in causing maritime accidents. Various studies have examined how alcohol affects critical skills, like navigation and decision making, as well as broader human factors that contribute to accidents.