IDEAS home Printed from https://ideas.repec.org/a/hin/jnljam/910196.html
   My bibliography  Save this article

A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

Author

Listed:
  • Fanping Zhang
  • Huichao Dai
  • Deshan Tang

Abstract

Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM) is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM) model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details ( ) and an approximation ( ) at three resolution levels (2 1 -2 2 -2 3 ) using Daubechies (db3) discrete wavelet. Correlation coefficients between each subtime series and original monthly streamflow time series are calculated. components with high correlation coefficients ( ) are added to the approximation ( ) as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters, , , and , of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.

Suggested Citation

  • Fanping Zhang & Huichao Dai & Deshan Tang, 2014. "A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-10, May.
  • Handle: RePEc:hin:jnljam:910196
    DOI: 10.1155/2014/910196
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/JAM/2014/910196.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/JAM/2014/910196.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2014/910196?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. Bulent Haznedar & Huseyin Cagan Kilinc & Furkan Ozkan & Adem Yurtsever, 2023. "Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 681-701, May.
    2. Xike Zhang & Qiuwen Zhang & Gui Zhang & Zhiping Nie & Zifan Gui & Huafei Que, 2018. "A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition," IJERPH, MDPI, vol. 15(5), pages 1-23, May.
    3. Fabio Di Nunno & Francesco Granata & Quoc Bao Pham & Giovanni de Marinis, 2022. "Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model," Sustainability, MDPI, vol. 14(5), pages 1-21, February.

    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:jnljam:910196. 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.