###### Abstract  
An integrative approach to maritime accident risk factor assessment in accordance with formal safety assessment is proposed, which exploits the multifaceted capabilities of Bayesian networks (BNs) by consolidation of modelling, verification, and validation. The methodology for probabilistic modelling with BNs is well known and its application to risk assessment is based on the model verified though sensitivity analysis only, while validation of the model is often omitted due to a lack of established evaluation measures applicable to scarce real-world data. For this reason, in this work, the modified Lyapunov divergence measure is proposed as a novel quantitative assessor that can be efficiently exploited on an individual accident scenario for contributing causal factor identification, and thus can serve as the measure for validation of the developed expert elicited BN. The proposed framework and its approach are showcased for maritime grounding of small passenger ships in the Adriatic, with the complete grounding model disclosed, quantitative validation performed, and its utilization for causal factor identification and risk factor ranking presented. The data from two real-world grounding cases demonstrate the explanatory capabilities of the developed approach. Bayesian belief network; Bayesian network validation; risk factor assessment; Bayesian inference; ship grounding model +
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and integration of historical data and expert knowledge and variable parametrization, verification of data flow consistency with expert expectations, and inference as the most important component of the framework. To avoid repeating good reviewing material with a detailed review of conceptual components of Bayesian belief network formalism and application for risk assessment in comparison to other approaches, the reader may refer to [12; 13]. An excellent overview of maritime accident research from a broad perspective over the past 50 years, using 572 peer-reviewed papers, was published recently [14]. In [15], a recent overview of BBN modelling for shipping accidents is given. While BBN modelling and sensitivity analysis-based verification has been previously employed and well established in maritime accident modelling, less attention has been given to investigation of the validation of maritime accident models built on the expert elicited BBN model. The goal of validation is to increase the reliability of a model when its purpose is to rank the factors that affect the accident or to exploit the model to examine the effect of action on individual observable or unobservable variables. Validation of the expert elicited Bayesian network is a difficult but required endeavor [16], upon which the validity of risk factor assessment depends. Qualitative validation approaches for this type of model are discussed in [17]; however, no clear procedure is available for quantitative validation of expert elicited BBN models. To this end, we develop a divergence-based measure for diagnostic inference that aims to quantitatively exploit each individual available real data case. While insufficient real case data for specific maritime accidents are scarce and cannot be efficiently deployed for learning-based accident model development, this work proposes to use it to test the diagnostic capabilities of an expert elicited model, giving in such a way to give quantitative insight into model behavior as the obtained data can be compared to the reported expert assessment, thus increasing the confidence in the developed model. Diagnostic inference is a promising analytic approach to identify possible causes, contributory factors, or ideally root cause of maritime accidents through forward-backward message passing in BBN; however, it has been underrepresented within maritime accident modelling and risk factor assessment due to a lack of established diagnostic inferential measures. To meet this shortcoming, we propose the Lyapunov-based divergence as a measure for identification of causal risk factors.