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影响因子：3.8

DOI码：10.1111/risa.14175

发表刊物：Risk Analysis

刊物所在地：UNITED STATES

关键字：Arctic navigation; Interpretable machine learning; Reproduction of expert judgments; Safety risk assessment

摘要：The Northern Sea Route (NSR) makes travel between Europe and Asia shorter and quicker than a southern transit via the Strait of Malacca and Suez Canal. It provides greater access to Arctic resources such as oil and gas. As global warming accelerates, melting Arctic ice caps are likely to increase traffic in the NSR and enhance its commercial viability. Due to the harsh Arctic environment imposing threats to the safety of ship navigation, it is necessary to assess Arctic navigation risk to maintain shipping safety. Currently, most studies are focused on the conventional assessment of the risk, which lacks the validation based on actual data. In this study, actual data about Arctic navigation environment and related expert judgments were used to generate a structured data set. Based on the structured data set, extreme gradient boosting (XGBoost) and alternative methods were used to establish models for the assessment of Arctic navigation risk, which were validated using cross-validation. The results show that compared with alternative models, XGBoost models have the best performance in terms of mean absolute errors and root mean squared errors. The XGBoost models can learn and reproduce expert judgments and knowledge for the assessment of Arctic navigation risk. Feature importance (FI) and shapley additive explanations (SHAP) are used to further interpret the relationship between input data and predictions. The application of XGBoost, FI, and SHAP is aimed to improve the safety of Arctic shipping using advanced artificial intelligence techniques. The validated assessment enhances the quality and robustness of assessment.

论文类型：期刊论文

学科门类：管理学

文献类型：J

ISSN号：0272-4332

是否译文：否

发表时间：2023-06-17

收录刊物：SCI(E)、SSCI

发布期刊链接：https://onlinelibrary.wiley.com/doi/10.1111/risa.14175?af=R