Maritime accident risk prediction integrating weather data using machine learning

Peter Brandt, Ziaul Haque Munim*, Meriam Chaal, Hooi Siang Kang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
61 Downloads (Pure)

Abstract

The study explores the capability of various machine learning (ML) models in maritime accident risk prediction. Data from 1981 to 2021 from the Norwegian Maritime Authorities (NMA) was analysed together with the data of 51 different weather-related variables, which were collected from Visual Crossing for each accident recorded in the NMA dataset. The findings reveal an increased predictive ability of ML models when relevant weather data is introduced. The results show that the Light Gradient Boosted Trees with Early Stopping perform the best, with a five-fold cross validation accuracy of 70.23% when weather data was included, compared to 64.86% without. Furthermore, the study revealed that the leading weather variables for accident prediction are wind, sea level pressure, visibility, and moon phase. The most effective multi-classification ML algorithm can be deployed for improving maritime safety resilience through vulnerability assessment and preparedness.

Original languageEnglish
Article number104388
Number of pages26
JournalTransportation Research, Part D: Transport and Environment
Volume136
DOIs
Publication statusPublished - Nov 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Accident analysis
  • Automated machine learning
  • Maritime safety
  • Risk assessment

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