Journal is indexed in following databases:

2022 Journal Impact Factor - 0.6
2022 CiteScore - 1.7




ISSN 2083-6473
ISSN 2083-6481 (electronic version)




Associate Editor
Prof. Tomasz Neumann

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
Using Bayesian Networks to Model Competence of Lifeboat Coxswains
Times cited (SCOPUS): 1
ABSTRACT: The assessment of lifeboat coxswain performance in operational scenarios representing offshore emergencies has been prohibitive due to risk. For this reason, human performance in plausible emergencies is difficult to predict due to the limited data that is available. The advent of lifeboat simulation provides a means to practice in weather conditions representative of an offshore emergency. In this paper, we present a methodology to create probabilistic models to study this new problem space using Bayesian Networks (BNs) to formulate a model of competence. We combine expert input and simulator data to create a BN model of the competence of slow-speed maneuvering (SSM). We demonstrate how the model is improved using data collected in an experiment designed to measure performance of coxswains in an emergency scenario. We illustrate how this model can be used to predict performance and diagnose background information about the student. The methodology demonstrates the use of simulation and probabilistic methods to increase domain awareness where limited data is available. We discuss how the methodology can be applied to improve predictions and adapt training using machine learning.
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Citation note:
Billard R., Smith J., Masharraf M., Veitch B.: Using Bayesian Networks to Model Competence of Lifeboat Coxswains. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 14, No. 3, doi:10.12716/1001.14.03.09, pp. 585-594, 2020
Authors in other databases:
Jennifer Smith:
Mashrura Masharraf:

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