IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/7840569.html
   My bibliography  Save this article

Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model

Author

Listed:
  • Peng Qin
  • Chunmei Cheng

Abstract

The analysis and prediction of seawall settlement are important for seawall engineering maintenance and disaster prevention. Based on the measured seawall settlement time series data, a combined LS-ARIMA forecasting model that fits the trend item by the least-square (LS) method and the season item by the differential self-regression moving average (ARIMA) model was proposed in this study. The monitoring data of one seawall project in Zhejiang, China, is taken as an example to verify the model efficiency and prediction ability. The results show that the prediction accuracy of the new combined LS-ARIMA model was high, with the average relative error (ARE) of 0.23%, much better than that of the traditional ARIMA model (ARE = 0.70%) and the GM (1, 1) model (ARE = 33.43%). This new model has clear physical conception and can effectively improve the prediction accuracy, implying that it can fully tap the dynamic information of monitoring data. The proposed model in this study provides a new research idea for data analysis and prediction of the seawall settlement.

Suggested Citation

  • Peng Qin & Chunmei Cheng, 2017. "Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-7, May.
    • Handle: RePEc:hin:jnlmpe:7840569
    DOI: 10.1155/2017/7840569
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2017/7840569.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2017/7840569.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2017/7840569?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sen Zheng & Chongshi Gu & Chenfei Shao & Yating Hu & Yanxin Xu & Xiaoyu Huang, 2023. "A Novel Prediction Model for Seawall Deformation Based on CPSO-WNN-LSTM," Mathematics, MDPI, vol. 11(17), pages 1-22, August.

    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:7840569. 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.