IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v39y2025i9d10.1007_s11269-025-04164-z.html
   My bibliography  Save this article

Uncertainty Analysis of Machine Learning Methods To Estimate Snow Water Equivalent Using Meteorological and Remote Sensing Data

Author

Listed:
  • Mohammad Reza Goodarzi

    (Ferdowsi University of Mashhad)

  • Ali Barzkar

    (Yazd University)

  • Maryam Sabaghzadeh

    (Yazd University)

  • Meysam ghanbari

    (Yazd University)

  • Nasrin Fathollahzadeh Attar

    (University of Padova)

Abstract

A better understanding of regional water resources may help improve management of these resources. Snow is a resource that cannot be accurately measured. Research in this field has been increasing in recent years with the advancements in machine learning methods and satellite imagery. This research measured Snow Water Equivalent (SWE) using eight machine learning approaches; Furthermore, the input data used in this study are monthly. Ten meteorological parameters related to snow, and also two parameters for albedo and snow cover fraction obtained from satellite imagery from 1981 to 2022, were provided as inputs to the models. The best input combination was determined through the gamma test, and the impact percentage of each input on the result was evaluated using sensitivity analysis. The results showed that the XGBoost (XGB) method with a Nash-Sutcliffe Efficiency (NSE) coefficient of 0.99 is the best, and Ridge regression with an NSE coefficient value of 0.6486 has the worst result. The dataset was divided into 80% for training and 20% for testing. The trend of snow water equivalent (SWE) during this period was evaluated utilizing the Mann-Kendall test and Sen’s slope analysis. The findings indicate a decreasing trend in SWE. The utilization of current machine learning methods helps a more precise estimation of the water volume in the region, providing improved decision-making for the management of regional water resources. Graphical Abstract

Suggested Citation

  • Mohammad Reza Goodarzi & Ali Barzkar & Maryam Sabaghzadeh & Meysam ghanbari & Nasrin Fathollahzadeh Attar, 2025. "Uncertainty Analysis of Machine Learning Methods To Estimate Snow Water Equivalent Using Meteorological and Remote Sensing Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(9), pages 4471-4491, July.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:9:d:10.1007_s11269-025-04164-z
    DOI: 10.1007/s11269-025-04164-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-025-04164-z
    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/s11269-025-04164-z?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:waterr:v:39:y:2025:i:9:d:10.1007_s11269-025-04164-z. 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.