Machine learning methods for predicting marine port accidents: a case study in container terminal

Üstün Atak, Yasin Arslanoğlu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Rapid changes in voyage orders and increased container throughput could lead to undesirable situations such as incidents or accidents in maritime ports. As the demand for maritime transport grows, historical accident reports and data-driven approaches could help to achieve safer and quicker door-to-door transportation. In this scope, the cargo operation data retrieved from the terminal operating system and accident reports are analysed using machine learning classification methods for two sample maritime container terminals located in Turkey. The calculation of accident prediction is studied with features such as vessel capacity, weather information, and cargo handling time. As a validation process, the second container terminal data is used for predicting operation-related accidents. The findings show that XGBoost, LightGBM, and KNN algorithms performed accident prediction with precision metrics of 0.98 for Terminal B and over 0.99–1 for Terminal A amongst the other machine learning classification methods for one-day intervals.

Original languageEnglish
Pages (from-to)2480-2487
Number of pages8
JournalShips and Offshore Structures
Volume17
Issue number11
DOIs
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.

Funding

This work was supported by the Research Fund of the Istanbul Technical University [project number MDK-2020-42697].

FundersFunder number
Istanbul Teknik ÜniversitesiMDK-2020-42697

    Keywords

    • artificial intelligence
    • Container terminal
    • risk management
    • supervised classification

    Fingerprint

    Dive into the research topics of 'Machine learning methods for predicting marine port accidents: a case study in container terminal'. Together they form a unique fingerprint.

    Cite this