Skip to main content

Intelligent Geospatial Maritime Risk Analytics Using The Discrete Global Grid System.

Rawson, A., Sabeur, Z. and Brito, M., 2021. Intelligent Geospatial Maritime Risk Analytics Using The Discrete Global Grid System. Big Earth Data. (In Press)

Full text available as:

[img]
Preview
PDF (OPEN ACCESS ARTICLE)
20964471.2021.pdf - Published Version
Available under License Creative Commons Attribution.

38MB

DOI: 10.1080/20964471.2021.1965370

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.

Item Type:Article
ISSN:2096-4471
Additional Information:This work is partly funded by the University of Southampton’s Marine and Maritime Institute (SMMI) and the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement number: 723526: SEDNA).
Uncontrolled Keywords:Maritime risk, Discrete Global Grid System, big data, machine learning
Group:Faculty of Science & Technology
ID Code:36030
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:20 Sep 2021 14:49
Last Modified:20 Sep 2021 14:49

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -