The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment

Round 1

Reviewer 1 Report

The authors propose an integrative approach to maritime accident risk factor assessment by Formal Safety Assessment that exploits the multifaceted capabilities of Bayesian Networks (BN) by the consolidation of modelling, verification and validation. In addition, the authors propose the modified Lyapunov divergence measure 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 approach are showcased for maritime grounding of small passenger ships in the Adriatic. A complete grounding model is disclosed, quantitative validation is performed, and its utilization for causal factor identification and risk factor ranking is presented. The data from two real-world grounding cases demonstrate the explanatory capabilities of the developed approach. There should be an extended conclusions section and future research directions. Also, the quality of some figures must be improved (the problem with being readable).

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

The submitted manuscript presents an application of Bayesian Belief Network to the investigation of maritime safety. Although the idea is not new, it does contain some novel aspects, such as application of Lyapunov divergence measures.

My comments are given below: 

Page 2: unnecessary paragraph added around line 81/82;

page 3, lines 102-103 reads: 'Although the methodology has been developed for a specific class of maritime accident and a vessel type'. This is unclear as the reader may not immediately know what methodology, accident, and vessel type is referred to - please provide a better explanation;

page 8, lines 317-330, please refer to a recently published paper under DOI: 10.1016/j.ress.2021.107942 for a thorough discussion of human error and factors;

Figures: 3.3., 3.5., 4.4., 5.1. - description of axis is missing and so it might be difficult to comprehend.

All in all, I recommend that the paper be accepted following a minor revision by the authors.

 

Author Response

Please see the attachment

Author Response File: Author Response.pdf