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A hybrid fuzzy system dynamics approach for risk analysis of AUV operations

Loh, Tzu Yang; Brito, Mario P.; Bose, Neil; Xu, Jingjing; Nikolova, Natalia; Tenekedjiev, Kiril


Tzu Yang Loh

Mario P. Brito

Neil Bose

Jingjing Xu

Natalia Nikolova

Kiril Tenekedjiev


© 2020 Fuji Technology Press. All rights reserved. The maturing of autonomous technology has fostered a rapid expansion in the use of Autonomous Underwater Vehicles (AUVs). To prevent the loss of AUVs during deployments, existing risk analysis approaches tend to focus on technicalities, historical data and experts’ opinion for probability quantification. However, data may not always be available and the complex interrelationships between risk factors are often neglected due to uncertainties. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. The approach utilises the strengths while overcoming limitations of both system dynamics and fuzzy set theory. Presented as a three-step iterative framework, the approach was applied on a case study to examine the impact of crew operating experience on the risk of AUV loss. Results showed not only that initial experience of the team affects the risk of loss, but any loss of experience in earlier stages of the AUV program have a lesser impact as compared to later stages. A series of risk control policies were recommended based on the results. The case study demonstrated how the FuSDRA approach can be applied to inform human resource and risk management strategies, or broader application within the AUV domain and other complex technological systems.


Loh, T. Y., Brito, M. P., Bose, N., Xu, J., Nikolova, N., & Tenekedjiev, K. (2020). A hybrid fuzzy system dynamics approach for risk analysis of AUV operations. Journal of Advanced Computational Intelligence and Intelligent Informatics, 24(1), 26-39.

Journal Article Type Article
Acceptance Date Nov 11, 2019
Online Publication Date Jan 20, 2020
Publication Date Jan 20, 2020
Deposit Date Mar 7, 2020
Journal Journal of Advanced Computational Intelligence and Intelligent Informatics
Print ISSN 1343-0130
Electronic ISSN 1883-8014
Peer Reviewed Peer Reviewed
Volume 24
Issue 1
Pages 26-39
Keywords Human-Computer Interaction; Artificial Intelligence; Computer Vision and Pattern Recognition
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