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.
Full text available as:
|
PDF
Sabeur et al 2021.pdf - Published Version Available under License Creative Commons Attribution Non-commercial. 4MB | |
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
DOI: 10.1016/j.ssci.2021.105336
Abstract
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 |
---|---|
ISSN: | 0925-7535 |
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 |
Downloads
Downloads per month over past year
Repository Staff Only - |