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Spatial modelling of maritime risk using machine learning

Spatial modelling of maritime risk using machine learning
Spatial modelling of maritime risk using machine learning
Managing navigational safety is a key responsibility of coastal states. Predicting and measuring these risks has a high complexity due to their infrequent occurrence, multitude of causes and large study areas. As a result, maritime risk models are generally limited in scale to small regions, generalised across diverse environments or rely on the use of expert judgement. Therefore, such an approach has limited scalability and may incorrectly characterise the risk. Within this paper a novel method for undertaking spatial modelling of maritime risk is proposed through machine learning. This enables navigational safety to be characterised whilst leveraging the significant volumes of relevant data available. The method comprises two key components: Aggregation of historical accident data, vessel traffic and other exploratory features into a spatial grid; and the implementation of several classification algorithms that predicts annual accident occurrence for various vessel types. This approach is applied to characterise the risk of collisions and groundings in the United Kingdom. The results vary between hazard types and vessel types but show remarkable capability at characterising maritime risk, with accuracies and AUC scores in excess of 90% in most implementations. Furthermore, the ensemble tree-based algorithms of XGBoost and Random Forest consistently outperformed other machine learning algorithms that were tested. The resultant potential risk maps provide decision makers with actionable intelligence in order to target risk mitigation measures in regions with the greatest requirement.
Maritime risk assessment,, machine learning, risk mapping
0272-4332
Rawson, Andrew, David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Sabeur, Zoheir
b44b6542-2fd3-4018-a6bf-20b6c0fbb397
Rawson, Andrew, David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Sabeur, Zoheir
b44b6542-2fd3-4018-a6bf-20b6c0fbb397

Rawson, Andrew, David, Brito, Mario and Sabeur, Zoheir (2021) Spatial modelling of maritime risk using machine learning. Risk Analysis. (doi:10.1111/risa.13866).

Record type: Article

Abstract

Managing navigational safety is a key responsibility of coastal states. Predicting and measuring these risks has a high complexity due to their infrequent occurrence, multitude of causes and large study areas. As a result, maritime risk models are generally limited in scale to small regions, generalised across diverse environments or rely on the use of expert judgement. Therefore, such an approach has limited scalability and may incorrectly characterise the risk. Within this paper a novel method for undertaking spatial modelling of maritime risk is proposed through machine learning. This enables navigational safety to be characterised whilst leveraging the significant volumes of relevant data available. The method comprises two key components: Aggregation of historical accident data, vessel traffic and other exploratory features into a spatial grid; and the implementation of several classification algorithms that predicts annual accident occurrence for various vessel types. This approach is applied to characterise the risk of collisions and groundings in the United Kingdom. The results vary between hazard types and vessel types but show remarkable capability at characterising maritime risk, with accuracies and AUC scores in excess of 90% in most implementations. Furthermore, the ensemble tree-based algorithms of XGBoost and Random Forest consistently outperformed other machine learning algorithms that were tested. The resultant potential risk maps provide decision makers with actionable intelligence in order to target risk mitigation measures in regions with the greatest requirement.

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More information

Accepted/In Press date: 9 November 2021
e-pub ahead of print date: 1 December 2021
Keywords: Maritime risk assessment,, machine learning, risk mapping

Identifiers

Local EPrints ID: 452247
URI: http://eprints.soton.ac.uk/id/eprint/452247
ISSN: 0272-4332
PURE UUID: af8999e0-1964-4ad0-8e32-d9554322e2b4
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: 02 Dec 2021 17:30
Last modified: 28 Apr 2022 02:25

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Contributors

Author: Andrew, David Rawson ORCID iD
Author: Mario Brito ORCID iD
Author: Zoheir Sabeur

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