An ensemble machine learning model for predicting the need for icebreaker assistance in ice-covered waters

Winter navigation presents challenges due to ice conditions, necessitating typical navigation modes: independent navigation and icebreaker assistance. Current navigation mode estimations rely on navigators' expertise, which is subjective and difficult to standardize. Motivated by the complexities of current estimations and the need for icebreaker resource optimization, this study proposes neural oblivious decision ensembles, a deep learning model, to estimate navigation modes based on ship characteristics and operational conditions. Given the inherently imbalanced data, where icebreaker assistance cases are fewer compared to independent navigations, the focal loss function is employed to emphasize the minority class. The results show that the proposed model outperforms benchmarks like random forest and gradient boosting, achieving 97 % accuracy, 95 % precision, 93 % recall, and 94 % F1 score, with up to a 10 % recall and 6 % F1 score improvement. By quantifying prediction probabilities and uncertainties, the model enables informed decision-making, where high-probability, low-uncertainty predictions can reliably guide estimations. The findings demonstrate that the proposed model can generate spatially scalable maps to highlight areas requiring assistance and provide granular estimates along ship routes. Predictions with understandable visual representations can support proactive icebreaker allocation. These insights lay the groundwork for developing an intelligent decision-support system and future resource optimization.