Arctic sea ice thickness prediction using machine learning: a long short-term memory model

Tarek Zaatar, Ali Cheaitou, Olivier Faury () and Patrick Rigot-Muller
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Tarek Zaatar: University of Sharjah
Ali Cheaitou: University of Sharjah
Olivier Faury: EM Normandie, Métis Lab
Patrick Rigot-Muller: Maynooth University

Annals of Operations Research, 2025, vol. 345, issue 1, No 18, 533-568

Abstract: Abstract This paper introduces and details the development of a Long Short-Term Memory (LSTM) model designed to predict Arctic ice thickness, serving as a decision-making tool for maritime navigation. By forecasting ice conditions accurately, the model aims to support safer and more efficient shipping through Arctic waters. The primary objective is to equip shipping companies and decision-makers with a reliable method for estimating ice thickness in the Arctic. This will enable them to assess the level of risk due to ice and make informed decisions regarding vessel navigation, icebreaker assistance, and optimal sailing speeds. We utilized historical ice thickness data from the Copernicus database, covering the period from 1991 to 2019. This dataset was collected and preprocessed to train and validate the LSTM predictive model for accurate ice thickness forecasting. The developed LSTM model demonstrated a high level of accuracy in predicting future ice thickness. Experiments indicated that using daily datasets, the model could forecast daily ice thickness up to 30 days ahead. With monthly datasets, it successfully predicted ice thickness up to six months in advance, with the monthly data generally yielding better performance. In practical terms, this predictive model offers a valuable tool for shipping companies exploring Arctic routes, which can reduce the distance between Asia and Europe by 40%. By providing accurate ice thickness forecasts, the model assists in compliance with the International Maritime Organization’s Polar Code and the Polar Operational Limit Assessment Risk Indexing System. This enhances navigation safety and efficiency in Arctic waters, allowing ships to determine the necessity of icebreaker assistance and optimal speeds, ultimately leading to significant cost savings and risk mitigation in the shipping industry.

Keywords: Neural networks; Long short-term memory (LSTM); Climate forecasting; Regional forecasting; Ice thickness; Northern sea route (NSR) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-024-06457-9

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