IDEAS home Printed from https://ideas.repec.org/a/igg/jsir00/v11y2020i3p19-29.html
   My bibliography  Save this article

Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

Author

Listed:
  • Nawaf N. Hamadneh

    (Saudi Electronic University, Saudi Arabia)

Abstract

In this study, the performance of adaptive multilayer perceptron neural network (MLPNN) for predicting the Dead Sea water level is discussed. Firefly Algorithm (FFA), as an optimization algorithm is used for training the neural networks. To propose the MLPNN-FFA model, Dead Sea water levels over the period 1810–2005 are applied to train MLPNN. Statistical tests evaluate the accuracy of the hybrid MLPNN-FFA model. The predicted values of the proposed model were compared with the results obtained by another method. The results reveal that the artificial neural network (ANN) models exhibit high accuracy and reliability for the prediction of the Dead Sea water levels. The results also reveal that the Dead Sea water level would be around -450 until 2050.

Suggested Citation

  • Nawaf N. Hamadneh, 2020. "Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 11(3), pages 19-29, July.
    • Handle: RePEc:igg:jsir00:v:11:y:2020:i:3:p:19-29
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSIR.2020070102
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Nawaf N. Hamadneh & Samer Atawneh & Waqar A. Khan & Khaled A. Almejalli & Adeeb Alhomoud, 2022. "Using Artificial Intelligence to Predict Students’ Academic Performance in Blended Learning," Sustainability, MDPI, vol. 14(18), pages 1-13, September.

    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:igg:jsir00:v:11:y:2020:i:3:p:19-29. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.