A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors-Reference-Cited by-同舟云学术

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A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors

Published:2024-03 Issue: Volume:243 Page:109779
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Kandel Rajesh^ORCID,Baroud Hiba^ORCID

Funder

National Science Foundation

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference49 articles.

1. The urgency of arctic change;Overland;Polar Sci,2019

2. Navigating the future of Arctic shipping;Arctic-Council,2021

3. Commercial Arctic shipping through the Northeast Passage: routes, resources, governance, technology, and infrastructure;Buixadé Farré;Polar Geogr,2014

4. Arctic shipping and risks: emergency categories and response capacities;Marchenko;TransNav: Int J Mar Navig Saf Sea Transp,2018

5. How to protect the Arctic as melting ice opens new shipping routes;Willmer,2018

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4. Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration;Reliability Engineering & System Safety;2024-11

5. Development of an improved Bayesian network method for maritime accident safety assessment based on multiscale scenario analysis theory;Reliability Engineering & System Safety;2024-11

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