IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/376010.html
   My bibliography  Save this article

New Optimal Weight Combination Model for Forecasting Precipitation

Author

Listed:
  • Song-shan Yang
  • Xiao-hua Yang
  • Rong Jiang
  • Yi-che Zhang

Abstract

In order to overcome the inaccuracy of the forecast of a single model, a new optimal weight combination model is established to increase accuracies in precipitation forecasting, in which three forecast submodels based on rank set pair analysis (R-SPA) model, radical basis function (RBF) model and autoregressive model (AR) and one weight optimization model based on improved real-code genetic algorithm (IRGA) are introduced. The new model for forecasting precipitation time series is tested using the annual precipitation data of Beijing, China, from 1978 to 2008. Results indicate the optimal weights were obtained by using genetic algorithm in the new optimal weight combination model. Compared with the results of R-SPA, RBF, and AR models, the new model can improve the forecast accuracy of precipitation in terms of the error sum of squares. The amount of improved precision is 22.6%, 47.4%, 40.6%, respectively. This new forecast method is an extension to the combination prediction method.

Suggested Citation

  • Song-shan Yang & Xiao-hua Yang & Rong Jiang & Yi-che Zhang, 2012. "New Optimal Weight Combination Model for Forecasting Precipitation," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-13, May.
    • Handle: RePEc:hin:jnlmpe:376010
    DOI: 10.1155/2012/376010
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2012/376010.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2012/376010.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2012/376010?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. Arvind Yadav & Premkumar Chithaluru & Aman Singh & Marwan Ali Albahar & Anca Jurcut & Roberto Marcelo Álvarez & Ramesh Kumar Mojjada & Devendra Joshi, 2022. "Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models," Mathematics, MDPI, vol. 10(22), pages 1-22, November.

    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:376010. 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.