An ensemble machine learning model for predicting the need for icebreaker assistance in ice-covered waters
Loading...
Access rights
openAccess
CC BY
CC BY
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
17
Series
Engineering Applications of Artificial Intelligence, Volume 158
Abstract
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.Description
Publisher Copyright: © 2025 The Authors
Other note
Citation
Liu, C, Suominen, M & Musharraf, M 2025, 'An ensemble machine learning model for predicting the need for icebreaker assistance in ice-covered waters', Engineering Applications of Artificial Intelligence, vol. 158, 111489. https://doi.org/10.1016/j.engappai.2025.111489