A Framework for Integrating Life-Safety and Environmental Consequences into Conventional Arctic Shipping Risk Models

An interpretable XGBoost-based approach for Arctic navigation risk assessment

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.

On the determination and rank for the environmental risk aspects for ship navigating in the Arctic based on big Earth data

The unique, ambiguous, and complex navigable environment determines the essential difference between Arctic shipping routes and conventional routes in regard to safety issues. To achieve a scientific understanding of the characteristics and variations of environmental risks involved in the Arctic shipping, it is essential to rationally address the uncertainty and incompleteness of environment-related risk information. In this study, fuzzy evidential reasoning is introduced to carry out multisource heterogeneous data fusion and spatiotemporal dynamic assessment of navigable environmental risks for Arctic shipping routes. Based on big Earth data collected from the European Center for Medium-Range Weather Forecasts, National Snow And Ice Data Center, National Center for Environmental Information, and University of Bremen from 2012 to 2019, a case study of the Northeast Passage is considered to demonstrate the feasibility of the proposed methodology. Finally, the results are described from three aspects: spatial distribution, temporal changes, and sensitivity analysis, with consideration of the entire passage and five marginal seas at the same time. Based on these findings, the prospect of application of big Earth data in risk assessment is further discussed from two aspects of knowledge acquisition by big data and risk analysis at different scales, to inspire sustainable development of Arctic shipping.