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Intelligent geospatial maritime risk analytics using the discrete global grid system

Intelligent geospatial maritime risk analytics using the discrete global grid system
Intelligent geospatial maritime risk analytics using the discrete global grid system
Each year, accidents involving ships result in significant loss of life, environmental pollution and economic losses. The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence. Yet, such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures. This paper proposes the use of the Discrete Global Grid System (DGGS) as an efficient and advantageous structure to integrate vessel traffic, metocean, bathymetric, infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings. Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach. A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002. The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures, targeted to regions with the highest risk. Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.
Discrete Global Grid System, Maritime risk, big data, machine learning
2096-4471
1-29
Rawson, Andrew, David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Sabeur, Zoheir
b44b6542-2fd3-4018-a6bf-20b6c0fbb397
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Rawson, Andrew, David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Sabeur, Zoheir
b44b6542-2fd3-4018-a6bf-20b6c0fbb397
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c

Rawson, Andrew, David, Sabeur, Zoheir and Brito, Mario (2021) Intelligent geospatial maritime risk analytics using the discrete global grid system. Big Earth Data, 1-29. (doi:10.1080/20964471.2021.1965370).

Record type: Article

Abstract

Each year, accidents involving ships result in significant loss of life, environmental pollution and economic losses. The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence. Yet, such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures. This paper proposes the use of the Discrete Global Grid System (DGGS) as an efficient and advantageous structure to integrate vessel traffic, metocean, bathymetric, infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings. Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach. A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002. The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures, targeted to regions with the highest risk. Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.

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e-pub ahead of print date: 11 September 2021
Published date: 13 September 2021
Keywords: Discrete Global Grid System, Maritime risk, big data, machine learning

Identifiers

Local EPrints ID: 451470
URI: http://eprints.soton.ac.uk/id/eprint/451470
ISSN: 2096-4471
PURE UUID: 807d6bee-f242-4905-bcdc-0ea4338d1d9a
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: 30 Sep 2021 16:30
Last modified: 28 Apr 2022 02:25

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Contributors

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

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