Intelligent geospatial analytics for maritime risk assessment

Abstract

Shipping is an essential component of the global economy, but every year accidents result in significant loss of life and environmental pollution. Navigating vessels might collide with one another, run aground or capsize amongst a multitude of challenges to operating at sea. As the number and sizes of vessels have increased, novel or autonomous technologies are adopted and new environments such as the Arctic are exploited, these risks are likely to increase. Coastal states, ports and developers have a responsibility to assess these risks, and where the risk is intolerably high, implement mitigation measures to reduce them. To support this, significant research has developed a field of maritime risk analysis, attempting to employ rigorous scientific study to quantifying the risk of maritime accidents. Such methods are diverse, yet have received criticism for their lack of methodological rigour, narrow scope and one-dimensional rather than spatial-temporal approach to risk. More broadly, there is a recognition that by combining different datasets together, novel techniques might lead to more robust and practicable risk analysis tools. This thesis contributes to this purpose. It argues that by integrating massive and heterogenous datasets related to vessel navigation, machine learning algorithms can be used to predict the relative likelihood of accident occurrence. Whilst such an approach has been adopted in other disciplines this remains relatively unexplored in maritime risk assessment. To achieve this, four aspects are investigated. Firstly, to enable fast and efficient integration of different spatial datasets, the Discrete Global Grid System has been trialled as the underlying spatial data structure in combination with the development of a scalable maritime data processing pipeline. Such an approach is shown to have numerous advantageous qualities, particular relevant to large scale spatial analysis, that addresses some of the limitations of the Modifiable Areal Unit Problem. Secondly, a national scale risk model was constructed for the United States using machine learning methods, providing high-resolution and reliable risk assessment. This supports both strategic planning of waterways and real-time monitoring of vessel transits. Thirdly, to overcome the infrequency of accidents, near-miss modelling was undertaken, however, the results were shown to only have partial utility. Finally, a comparison is made of various conventional and machine methodologies, identifying that whilst the latter are often more complex, they address some failings in conventional methods. The results demonstrate the potential of these methods as a novel form of maritime risk analysis, supporting decision makers and contributing to improving the safety of vessels and the protection of the marine environment.

More information

Identifiers

ORCID for Andrew, David Rawson: orcid.org/0000-0002-8774-2415

ORCID for Timothy Norman: orcid.org/0000-0002-6387-4034

Catalogue record

Export record

Altmetrics