Intelligent geospatial maritime risk analytics using the Discrete Global Grid System
| Autor: | Zoheir Sabeur, Andrew David Rawson, Mario Brito |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Geospatial analysis
Metocean Mean squared error Computer science 0211 other engineering and technologies Environmental pollution 02 engineering and technology computer.software_genre Reduction (complexity) big data 0502 economics and business Geography. Anthropology. Recreation 14. Life underwater Computers in Earth Sciences 021101 geological & geomatics engineering discrete global grid system Structure (mathematical logic) QE1-996.5 050210 logistics & transportation 05 social sciences Geology maritime risk Data structure Computer Science Applications Random forest machine learning 13. Climate action Data mining computer |
| Zdroj: | Big Earth Data, Vol 0, Iss 0, Pp 1-29 (2021) |
| ISSN: | 2574-5417 2096-4471 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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