IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v119y2023i3d10.1007_s11069-023-06211-7.html
   My bibliography  Save this article

Application of random forest (RF) for flood levels prediction in Lower Ogun Basin, Nigeria

Author

Listed:
  • O. O. Aiyelokun

    (University of Ibadan)

  • O. D. Aiyelokun

    (Olivearc Solutions)

  • O. A. Agbede

    (University of Ibadan)

Abstract

This study evaluates the performance of random forest (RF) for predicting flood levels in the Lower Ogun Basin, Southwest Nigeria. Daily flood levels for a period of 36 years (1981 to 2016), recorded at Mokoloki weir, were obtained from the Ogun–Oshun River Basin Development Authority (OORBDA). Descriptive statistics were employed to provide concise information on the flood levels, and trend and autocorrelation assessments were performed using the Mann–Kendall test and the Ljung–Box test, respectively, at 95% confidence level. Antecedent daily flood levels of up to 7 days were selected as input features for the RF model to predict daily flood levels. To develop the RF model, the dataset was divided into train (70%), validation (15%), and test (15%). The performance of the RF model was evaluated using Mean Absolute Error (MAE), coefficient of determination (R2), Nash–Sutcliffe Efficiency Coefficient (NSEC), and Kling-Gupta efficiency (KGE). The study reveals that the highest flood level was 9.5 m, while 75% of the records were less or equal to 7.04 m. The flood level had a significant positive trend (tau = 0.19, 2-sided p value

Suggested Citation

  • O. O. Aiyelokun & O. D. Aiyelokun & O. A. Agbede, 2023. "Application of random forest (RF) for flood levels prediction in Lower Ogun Basin, Nigeria," 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. 119(3), pages 2179-2195, December.
    • Handle: RePEc:spr:nathaz:v:119:y:2023:i:3:d:10.1007_s11069-023-06211-7
    DOI: 10.1007/s11069-023-06211-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-06211-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-023-06211-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:nathaz:v:119:y:2023:i:3:d:10.1007_s11069-023-06211-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.