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A machine learning approach for monitoring ship safety in extreme weather events.

Rawson, A., Brito, M., Sabeur, Z. and Tran-Thanh, L., 2021. A machine learning approach for monitoring ship safety in extreme weather events. Safety Science, 141, 1-11.

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Sabeur et al 2021.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1016/j.ssci.2021.105336


Extreme weather events can result in loss of life, environmental pollution and major damage to vessels caught in their path. Many methods to characterise this risk have been proposed, however, they typically utilise deterministic thresholds of wind and wave limits which might not accurately reflect risk. To address this limitation, we investigate the potential of machine learning algorithms to quantify the relative likelihood of an incident during the US Atlantic hurricane season. By training an algorithm on vessel traffic, weather and historical casualty data, accident candidates can be identified from historic vessel tracks. Amongst the various methods tested, Support Vector Machines showed good performance with Recall at 95% and Accuracy reaching 92%. Finally, we implement the developed model using a case study of Hurricane Matthew (October 2016). Our method contributes to enhancements in maritime safety by enabling machine intelligent risk-aware ship routing and monitoring of vessel transits by Coastguard agencies.

Item Type:Article
Additional Information:Cited By :1 Export Date: 25 October 2021 Received 22 July 2020, Revised 10 March 2021, Accepted 4 May 2021, Available online 28 May 2021, Version of Record 28 May 2021.
Uncontrolled Keywords:Maritime risk assessment; Navigation safety; Machine learning; Severe weather events
Group:Faculty of Science & Technology
ID Code:37812
Deposited By: Symplectic RT2
Deposited On:16 Nov 2022 10:05
Last Modified:16 Nov 2022 10:05


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