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.

    References listed on IDEAS

    as
    1. Vesna Đukić & Zoran Radić, 2016. "Sensitivity Analysis of a Physically Based Distributed Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1669-1684, March.
    2. Hansheng Wang & Guodong Li & Chih‐Ling Tsai, 2007. "Regression coefficient and autoregressive order shrinkage and selection via the lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(1), pages 63-78, February.
    3. Safar Marofi & Hossein Tabari & Hamid Abyaneh, 2011. "Predicting Spatial Distribution of Snow Water Equivalent Using Multivariate Non-linear Regression and Computational Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1417-1435, March.
    4. Muhammad S. Ashraf & Ijaz Ahmad & Noor M. Khan & Fan Zhang & Ahmed Bilal & Jiali Guo, 2021. "Streamflow Variations in Monthly, Seasonal, Annual and Extreme Values Using Mann-Kendall, Spearmen’s Rho and Innovative Trend Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 243-261, January.
    5. Vesna Đukić & Zoran Radić, 2016. "Sensitivity Analysis of a Physically Based Distributed Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1669-1684, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. F. Wang & G. H. Huang & Y. Fan & Y. P. Li, 2020. "Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3199-3217, August.
    2. Saritha Padiyedath Gopalan & Akira Kawamura & Hideo Amaguchi & Gubash Azhikodan, 2020. "A Generalized Storage Function Model for the Water Level Estimation Using Rating Curve Relationship," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(8), pages 2603-2619, June.
    3. Yanlin Tang & Xinyuan Song & Zhongyi Zhu, 2015. "Variable selection via composite quantile regression with dependent errors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(1), pages 1-20, February.
    4. Jun Zhu & Hsin‐Cheng Huang & Perla E. Reyes, 2010. "On selection of spatial linear models for lattice data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 389-402, June.
    5. repec:hum:wpaper:sfb649dp2016-047 is not listed on IDEAS
    6. Shanshan Wen & Buda Su & Jinlong Huang & Yanjun Wang & Simon Treu & Fushuang Jiang & Shan Jiang & Han Jiang, 2024. "Attribution of streamflow changes during 1961–2019 in the Upper Yangtze and the Upper Yellow River basins," Climatic Change, Springer, vol. 177(4), pages 1-20, April.
    7. Noa Ohana-Levi & Yishai Netzer, 2023. "Long-Term Trends of Global Wine Market," Agriculture, MDPI, vol. 13(1), pages 1-26, January.
    8. Anders Bredahl Kock, 2012. "On the Oracle Property of the Adaptive Lasso in Stationary and Nonstationary Autoregressions," CREATES Research Papers 2012-05, Department of Economics and Business Economics, Aarhus University.
    9. Audrino, Francesco & Camponovo, Lorenzo, 2013. "Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models," Economics Working Paper Series 1327, University of St. Gallen, School of Economics and Political Science.
    10. Fabio Di Nunno & Marco De Matteo & Giovanni Izzo & Francesco Granata, 2023. "A Combined Clustering and Trends Analysis Approach for Characterizing Reference Evapotranspiration in Veneto," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    11. Xinyang Wang & Dehui Wang & Kai Yang, 2021. "Integer-valued time series model order shrinkage and selection via penalized quasi-likelihood approach," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 713-750, July.
    12. Medeiros, Marcelo C. & Mendes, Eduardo F., 2016. "ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 191(1), pages 255-271.
    13. Qiu, Jia & Li, Degao & You, Jinhong, 2015. "SCAD-penalized regression for varying-coefficient models with autoregressive errors," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 100-118.
    14. Panxu Yuan & Xiao Guo, 2022. "High-dimensional inference for linear model with correlated errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 21-52, January.
    15. Xie, Fang & Xu, Lihu & Yang, Youcai, 2017. "Lasso for sparse linear regression with exponentially β-mixing errors," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 64-70.
    16. Mihee Lee & Haipeng Shen & Jianhua Z. Huang & J. S. Marron, 2010. "Biclustering via Sparse Singular Value Decomposition," Biometrics, The International Biometric Society, vol. 66(4), pages 1087-1095, December.
    17. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    18. Marcelo C. Medeiros & Eduardo F. Mendes, 2012. "Estimating High-Dimensional Time Series Models," CREATES Research Papers 2012-37, Department of Economics and Business Economics, Aarhus University.
    19. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    20. Philip Kostov & Thankom Arun & Samuel Annim, 2014. "Financial Services to the Unbanked: the case of the Mzansi intervention in South Africa," Contemporary Economics, Vizja University, vol. 8(2), June.
    21. Hai-Long Shen & Xu Tang, 2021. "The PPADMM Method for Solving Quadratic Programming Problems," Mathematics, MDPI, vol. 9(9), pages 1-15, April.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.