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Modelling of ship navigation in extreme weather events using machine learning

Modelling of ship navigation in extreme weather events using machine learning
Modelling of ship navigation in extreme weather events using machine learning
Extreme weather events such as hurricanes are a significant hazard to shipping. We show that traditional methods to model weather related risks using naval architecture or historical incidents fail to accurately predict the potential risk of an accident by failing to account for risk mitigation actions taken by the bridge team. We therefore propose the use of unsupervised machine learning to identify clusters in risk response by ships to perceived high risk scenarios. This risk classification method is based on the analysis of large heterogenous datasets including vessel traffic, metocean and hurricane path data from the US Atlantic Hurricane Season. Clusters in vessel behaviour to these storms are identified and the risk perception by storm severity compared. The results of this analysis can be used to better understand the impact of extreme weather events on navigation safety and develop an early warning system for coast guard search and rescue response.
Research Publlishing
Rawson, Andrew David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Rawson, Andrew David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c

Rawson, Andrew David and Brito, Mario (2020) Modelling of ship navigation in extreme weather events using machine learning. In Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment And Management Conference. Research Publlishing. 8 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Extreme weather events such as hurricanes are a significant hazard to shipping. We show that traditional methods to model weather related risks using naval architecture or historical incidents fail to accurately predict the potential risk of an accident by failing to account for risk mitigation actions taken by the bridge team. We therefore propose the use of unsupervised machine learning to identify clusters in risk response by ships to perceived high risk scenarios. This risk classification method is based on the analysis of large heterogenous datasets including vessel traffic, metocean and hurricane path data from the US Atlantic Hurricane Season. Clusters in vessel behaviour to these storms are identified and the risk perception by storm severity compared. The results of this analysis can be used to better understand the impact of extreme weather events on navigation safety and develop an early warning system for coast guard search and rescue response.

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Published date: 2020

Identifiers

Local EPrints ID: 442606
URI: http://eprints.soton.ac.uk/id/eprint/442606
PURE UUID: 426cc208-20e9-4329-aeaa-c3c3d5b2648e
ORCID for Andrew David Rawson: ORCID iD orcid.org/0000-0002-8774-2415
ORCID for Mario Brito: ORCID iD orcid.org/0000-0002-1779-4535

Catalogue record

Date deposited: 21 Jul 2020 16:30
Last modified: 13 Dec 2021 03:30

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Contributors

Author: Andrew David Rawson ORCID iD
Author: Mario Brito ORCID iD

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