利用机器学习预测北极海冰厚度:长短期记忆模型

IF 4.4 3区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Tarek Zaatar, Ali Cheaitou, Olivier Faury, Patrick Rigot-Muller
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引用次数: 0

摘要

本文介绍并详细介绍了长短期记忆(LSTM)模型的发展,该模型旨在预测北极冰层厚度,作为海上航行的决策工具。通过准确预测冰况,该模型旨在支持通过北极水域的更安全、更高效的航运。主要目标是为航运公司和决策者提供一种可靠的方法来估计北极的冰层厚度。这将使他们能够评估因冰造成的风险水平,并就船舶导航、破冰船援助和最佳航行速度做出明智的决定。我们使用了哥白尼数据库中1991年至2019年期间的历史冰厚数据。对该数据集进行预处理,训练并验证LSTM预测模型对冰厚的准确预测。所建立的LSTM模式在预测未来冰厚方面具有较高的精度。实验表明,使用每日数据集,该模式可以预测30天内的每日冰厚。使用每月的数据集,它可以提前六个月成功预测冰的厚度,每月的数据通常会产生更好的效果。实际上,这个预测模型为探索北极航线的航运公司提供了一个有价值的工具,可以将亚洲和欧洲之间的距离缩短40%。通过提供准确的冰厚预测,该模式有助于遵守国际海事组织的极地规则和极地操作极限评估风险索引系统。这提高了北极水域航行的安全性和效率,使船舶能够确定破冰船辅助的必要性和最佳速度,最终为航运业节省大量成本并降低风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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.

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来源期刊
Annals of Operations Research
Annals of Operations Research 管理科学-运筹学与管理科学
CiteScore
7.90
自引率
16.70%
发文量
596
审稿时长
8.4 months
期刊介绍: The Annals of Operations Research publishes peer-reviewed original articles dealing with key aspects of operations research, including theory, practice, and computation. The journal publishes full-length research articles, short notes, expositions and surveys, reports on computational studies, and case studies that present new and innovative practical applications. In addition to regular issues, the journal publishes periodic special volumes that focus on defined fields of operations research, ranging from the highly theoretical to the algorithmic and the applied. These volumes have one or more Guest Editors who are responsible for collecting the papers and overseeing the refereeing process.
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