A machine learning approach for monitoring ship safety in extreme weather events
Authors: Rawson, A., Brito, M., Sabeur, Z. and Tran-Thanh, L.
Journal: Safety Science
Volume: 141
eISSN: 1879-1042
ISSN: 0925-7535
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.
https://eprints.bournemouth.ac.uk/37812/
Source: Scopus
A machine learning approach for monitoring ship safety in extreme weather events
Authors: Rawson, A., Brito, M., Sabeur, Z. and Tran-Thanh, L.
Journal: Safety Science
Volume: 141
DOI: 10.1016/j.ssci.2021.105336
https://eprints.bournemouth.ac.uk/37812/
Source: Manual
A machine learning approach for monitoring ship safety in extreme weather events
Authors: Rawson, A., Brito, M., Sabeur, Z. and Tran-Thanh, L.
Journal: Safety Science
Volume: 141
Pages: 1-11
ISSN: 0925-7535
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.
https://eprints.bournemouth.ac.uk/37812/
Source: BURO EPrints