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From conventional to machine learning methods for maritime risk assessment

From conventional to machine learning methods for maritime risk assessment
From conventional to machine learning methods for maritime risk assessment
Within the last thirty years, the range and complexity of methodologies proposed to assess maritime risk have increased significantly. Techniques such as expert judgement, incident analysis, geometric models, domain analysis and Bayesian Networks amongst many others have become dominant within both the literature and industry. On top of this, advances in machine learning algorithms and big data have opened opportunities for new methods which might overcome some limitations of conventional approaches. Yet, determining the suitability or validity of one technique over another is challenging as it requires a systematic multicriteria approach to compare the inputs, assumptions, methodologies and results of each method. Within this paper, such an approach is proposed and tested within an isolated waterway in order to justify the proposed advantages of a machine learning approach to maritime risk assessment and should serve as inspiration for future work.
Rawson, Andrew, David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Brito, Mario
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Sabeur, Zoheir
e6e98155-eadb-4b0f-ba88-ba5c313f0e24
Tran-Thanh, Long
aecacf50-460e-410a-83be-b0c2a5ae226e
Rawson, Andrew, David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Sabeur, Zoheir
e6e98155-eadb-4b0f-ba88-ba5c313f0e24
Tran-Thanh, Long
aecacf50-460e-410a-83be-b0c2a5ae226e

Rawson, Andrew, David, Brito, Mario, Sabeur, Zoheir and Tran-Thanh, Long (2021) From conventional to machine learning methods for maritime risk assessment. In Proceedings of the 14th International Conference On Marine Navigation And Safety of Sea Transportation. vol. 15 (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

Within the last thirty years, the range and complexity of methodologies proposed to assess maritime risk have increased significantly. Techniques such as expert judgement, incident analysis, geometric models, domain analysis and Bayesian Networks amongst many others have become dominant within both the literature and industry. On top of this, advances in machine learning algorithms and big data have opened opportunities for new methods which might overcome some limitations of conventional approaches. Yet, determining the suitability or validity of one technique over another is challenging as it requires a systematic multicriteria approach to compare the inputs, assumptions, methodologies and results of each method. Within this paper, such an approach is proposed and tested within an isolated waterway in order to justify the proposed advantages of a machine learning approach to maritime risk assessment and should serve as inspiration for future work.

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Accepted/In Press date: 15 June 2021

Identifiers

Local EPrints ID: 449867
URI: http://eprints.soton.ac.uk/id/eprint/449867
PURE UUID: a8b132b9-97c3-4155-af0a-03efbbbffdd0
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

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Date deposited: 23 Jun 2021 16:30
Last modified: 09 Jan 2022 04:05

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Contributors

Author: Andrew, David Rawson ORCID iD
Author: Mario Brito ORCID iD
Author: Zoheir Sabeur
Author: Long Tran-Thanh

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