IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/8823322.html
   My bibliography  Save this article

Ship Accident Prediction Based on Improved Quantum-Behaved PSO-LSSVM

Author

Listed:
  • Tian Chai
  • Han Xue
  • Kaibiao Sun
  • Jinxian Weng

Abstract

Water transportation plays an important role in the comprehensive transportation system and regional logistics. The number of vessel accidents is an important indicator for evaluating vessel traffic safety and the efficiency of the maritime management strategy. The aim of this work is to provide an efficient way to predict the number of vessel accidents in China. Firstly, to weaken the randomness of the vessel accident number time series, the gray processing operation is adopted to generate a new sequence with exponential and approximate exponential rules. In addition, an extended least-squares support vector machine (LSSVM) model is applied in the forecasting of the new sequence, in which the parameters of the LSSVM are optimized by an improved quantum-behaved particle swarm (IQPSO). The proposed method is applied in the forecasting of the number of vessel accidents in China, and the efficiency is shown by comparing the prediction results with GM (1, 1), PSO-LSSVM, and QPSO-LSSVM.

Suggested Citation

  • Tian Chai & Han Xue & Kaibiao Sun & Jinxian Weng, 2020. "Ship Accident Prediction Based on Improved Quantum-Behaved PSO-LSSVM," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, December.
    • Handle: RePEc:hin:jnlmpe:8823322
    DOI: 10.1155/2020/8823322
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2020/8823322.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2020/8823322.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/8823322?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:8823322. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.