Work

Maritime accident risk prediction integrating weather data using machine learning

Year: 2024

Type: article

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.

Source: Transportation Research Part D Transport and Environment

DOI: 10.1016/j.trd.2024.104388

Authors: Peter Brandt, Ziaul Haque Munim, Meriam Chaal, Hooi-Siang Kang

Institutions: University of South-Eastern Norway, Kotka Maritime Research Centre, Aalto University, University of Technology Malaysia

Language: English

Topic: Maritime Navigation and Safety

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $3,100