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Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches

Author

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  • Jayashree T R

    (Manipal Institute of Technology, Manipal Academy of Higher Education)

  • NV Subba Reddy

    (Department of Information Technology Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education)

  • U Dinesh Acharya

    (Manipal Institute of Technology, Manipal Academy of Higher Education)

Abstract

The increasing frequency of droughts and floods due to climate change has severely affected water resources across the globe in recent years. An optimal design for the scheduling and management of irrigation is thus urgently needed to adapt agricultural activities to the changing climate. The accurate estimation of reference crop evapotranspiration (ET0), a vital hydrological component of the water balance and crop water need, is a tiresome task if all the relevant climatic variables are unavailable. This study investigates the potential of four ensemble techniques for estimating precise values of the daily ET0 at representative stations in 10 agro-climatic zones in the state of Karnataka, India, from 1979 to 2014. The performance of these models was evaluated by using several combinations of climatic variables as inputs by using tenfold cross-validation. The outcomes indicated that predictions of ET0 by all four ensemble models based on all climatic variables were the most accurate in comparison with other input combinations. The random forest regressor was found to deliver the best performance among the four models on all measures considered (Nash–Sutcliffe efficiency, 1.0, root-mean-squared error, 0.016 mm/day, and mean absolute error, 0.011 mm/day). However, it incurred the highest computational cost, whereas the computational cost of the bagging model for linear regression was the lowest. The extreme gradient-boosting model delivered the most stable performance with a modified training dataset. The work here shows that these models can be recommended for daily ET0 estimation based on the users’ interests.

Suggested Citation

  • Jayashree T R & NV Subba Reddy & U Dinesh Acharya, 2023. "Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1013-1032, February.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:3:d:10.1007_s11269-022-03399-4
    DOI: 10.1007/s11269-022-03399-4
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    References listed on IDEAS

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    1. Qinghe Wang & Na Liu & Shunqing Zhong & Wulin Jiang, 2024. "Application and Uncertainty Analysis of Data-Driven and Process-Based Evapotranspiration Models Across Various Ecosystems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(7), pages 2359-2376, May.
    2. Abhirup Paria & Arindam Giri & Subrata Dutta & Sarmistha Neogy, 2025. "Smart Irrigation for Coriander Plant: Saving Water with AI and IoT," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(7), pages 3379-3395, May.
    3. Ismail Abd-Elaty & N. L. Kushwaha & Abhishek Patel, 2023. "Novel Hybrid Machine Learning Algorithms for Lakes Evaporation and Power Production using Floating Semitransparent Polymer Solar Cells," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4639-4661, September.
    4. Jitendra Rajput & Man Singh & K. Lal & Manoj Khanna & A. Sarangi & J. Mukherjee & Shrawan Singh, 2024. "Data-driven reference evapotranspiration (ET0) estimation: a comparative study of regression and machine learning techniques," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(5), pages 12679-12706, May.

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