A Dynamic Bayesian Network model for ship navigation risk in the Arctic Northeast Passage

Abstract

The melting of a significant amount of sea ice has enabled the commercial and normal operation of ships in the Arctic. However, the harsh natural environment, unique geographical location and complex navigational conditions pose a serious challenge to the safety of ships during navigation. Ship besetting in ice and ship-ice collision are identified as the most critical risks, potentially resulting in vessel damage, property loss, environmental harm, and even casualties. This study proposes a dynamic Bayesian network (DBN) model to assess and predict the risk of ship besetting in ice and ship-ice collision in five sea areas along the Arctic Northeast Passage. The proposed method comprehensively considers environmental, ship and human factors and incorporates multi-year (2013–2022) marine meteorological reanalysis data and expert knowledge. The developed model has seven time steps and can be updated based on past, current and future conditions. The results indicate that it can effectively reflect the distribution of ship navigation risks in various sea areas and has the capability to be updated in real-time, serving as a tool for guiding ship operations and navigation decisions. At the same time, this study confirms August and September are the optimal navigation periods for the Northeast Passage, and the East Siberian Sea presenting the highest navigation risk. Ice thickness, ice concentration, navigation experience, professional skills, ship speed and ship class are identified as the primary risk factors in the region.