Maritime accident risk prediction integrating weather data using machine learning

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment Pub Date : 2024-09-13 DOI:10.1016/j.trd.2024.104388

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引用次数: 0

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.

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利用机器学习整合气象数据进行海上事故风险预测

该研究探讨了各种机器学习（ML）模型在海事事故风险预测方面的能力。研究分析了挪威海事局（NMA）1981 年至 2021 年的数据以及 51 种不同的天气相关变量数据，这些数据是针对 NMA 数据集中记录的每起事故从视觉交叉（Visual Crossing）中收集的。研究结果表明，当引入相关天气数据时，ML 模型的预测能力有所提高。结果表明，具有早期停止功能的轻梯度提升树表现最佳，在包含天气数据时，其五倍交叉验证准确率为 70.23%，而不包含天气数据时为 64.86%。此外，研究还发现，事故预测的主要天气变量是风、海平面气压、能见度和月相。最有效的多分类 ML 算法可用于通过脆弱性评估和准备工作提高海事安全复原力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Transportation Research Part D-transport and Environment 工程技术-运输科技

CiteScore

14.40

自引率

9.20%

发文量

314

审稿时长

39 days

期刊介绍： Transportation Research Part D: Transport and Environment focuses on original research exploring the environmental impacts of transportation, policy responses to these impacts, and their implications for transportation system design, planning, and management. The journal comprehensively covers the interaction between transportation and the environment, ranging from local effects on specific geographical areas to global implications such as natural resource depletion and atmospheric pollution. We welcome research papers across all transportation modes, including maritime, air, and land transportation, assessing their environmental impacts broadly. Papers addressing both mobile aspects and transportation infrastructure are considered. The journal prioritizes empirical findings and policy responses of regulatory, planning, technical, or fiscal nature. Articles are policy-driven, accessible, and applicable to readers from diverse disciplines, emphasizing relevance and practicality. We encourage interdisciplinary submissions and welcome contributions from economically developing and advanced countries alike, reflecting our international orientation.