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Cited by 107Maritime Transport Emissions and EfficiencyMaritime Navigation and SafetyMaritime Ports and Logistics

A novel, data-driven heuristic framework for vessel weather routing

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Gkerekos, Christos, Lazakis, Iraklis·2020·Ocean Engineering
DOI 10.1016/j.oceaneng.2019.106887
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  • AI and ML algorithms can be used to analyze weather data to identify the safest and most efficient routes for ships to take, taking into account factors such as wind speed, wave height, and water currents.

    “AI and ML algorithms can be used to analyze weather data to identify the safest and most efficient routes for ships to take, taking into account factors such as wind speed, wave height, and water currents [109] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [109]

    The Conclusions describe a data-driven route-selection framework using weather forecasts, and §4.2 identifies optimal routes by fuel-oil consumption. Table 2 lists wave heights and ocean currents among the model inputs. The cited passages do not establish that the framework selects the safest routes or uses wind speed as an input. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cites

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    “Whereas parameters are elements of the model that are directly learnt from the training data, hyperparameters cannot180 be directly estimated from the training data as there is no analytical formula available to calculate an appropriate value (Kuhn and Johnson, 2013).”
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Cited by

Sorted by citations

Authors
Gkerekos, C., & Lazakis, I.
Affiliations
University of Strathclyde (GB)
Corresponding author
Gkerekos, Christos
ORCID
Venue
Ocean Engineering
Publisher
Elsevier BV
Published
2020-02
Volume
197
Pages
106887
ISSN
0029-8018 (ISSN-L)
DOI
10.1016/j.oceaneng.2019.106887
OpenAlex
W2999082917
Cited by
107
References
1
Citations by year
2026: 11 · 2025: 28 · 2024: 15 · 2023: 17 · 2022: 21 · 2021: 11 · 2020: 4
FWCI
4.63
Type
journal article
Language
en
Open access
Licence
Locations
  • Ocean Engineering (publishedVersion)
  • Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
Funders
Innovate UK (102435)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Maritime Transport Emissions and Efficiency
  • Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Maritime Ports and Logistics
Keywords
fuel oil consumption, Dijkstra's algorithm, heuristic search, crude oil tankers, sailing speed, maritime emissions reduction

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