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Publications
Liu, Y.J., Lu, Y., Xue, Y.Z. and Guedes Soares, C. (2025), Prediction of navigation risks in the Arctic Northeast Passage based on Bayesian networks and neural networks, Reliability Engineering and System Safety, Vol. 267, 111791.
This study proposes a hybrid method for predicting and analysing navigation risks in the Arctic Northeast Passage, as global warming has led to significant melting of Arctic sea ice, resulting in increased traffic along this route. Thus, ensuring the safe navigation of ships in ice-covered waters has become a critical issue. Specifically, a Bayesian network risk assessment model is developed to quantify the risks associated with ship besetting in ice and ship-ice collisions. The model comprehensively incorporates environmental, ship-related, human, and organisational factors. To address the uncertainty arising from the limited availability of accident data and the subjective judgments of experts, probability distributions and Monte Carlo methods are employed for uncertainty analysis of navigation risks. Additionally, this method integrates a neural network to predict navigation risks. The proposed model demonstrates strong predictive performance and accurately estimates risk levels under varying conditions, supporting operational decision-making for Arctic voyages. Spatial visualisation of predicted risks across different scenarios further confirms the model’s applicability. Overall, the framework offers a data-driven and practical tool to enhance route planning and improve navigational safety in Arctic waters.
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