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Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm

Author

Listed:
  • Yash Agrawal

    (Gramworkx Agrotech Pvt Ltd - GramworkX, Keonics)

  • Manoranjan Kumar

    (Central Research Institute for Dryland Agriculture)

  • Supriya Ananthakrishnan

    (Gramworkx Agrotech Pvt Ltd - GramworkX, Keonics)

  • Gopalakrishnan Kumarapuram

    (Gramworkx Agrotech Pvt Ltd - GramworkX, Keonics)

Abstract

The present study investigates and evaluate the scope and potential of modern computing tools and techniques such as ensembled machine learning methods in estimating ETo. Five different type of machine learning model namely (i) decision tree, (ii) Random Forest (RF), (iii) Adaptive Boosting (AdaBoost), (iv) Gradient Boosting Machine (GBM) and (v) Extreme Gradient Boosting (XGBoost) were compared for performance in estimating daily P-M ETo values. The RF, GBM and XGBoost model performed extremely well on the criteria of weighted standard error of estimate (WSEE) which is less than 0.25 mm/d. Furthermore, the ensembled machine learning model substantiated by boosting algorithm (XGBoost) significantly enhance the performance in estimating P-M ETo (WSEE is less than 0.17 mm/d). Moreover, the sensitivity analysis suggested that the data requirement for XGBoost is commonly available at most of the places unlike P-M ETo model. Given the generalization capability of the model, it can be successfully implemented for other similar location where comprehensive data are not available.

Suggested Citation

  • Yash Agrawal & Manoranjan Kumar & Supriya Ananthakrishnan & Gopalakrishnan Kumarapuram, 2022. "Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1025-1042, February.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:3:d:10.1007_s11269-022-03067-7
    DOI: 10.1007/s11269-022-03067-7
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    References listed on IDEAS

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    1. Sarita Gajbhiye Meshram & Vijay P. Singh & Ozgur Kisi & Vahid Karimi & Chandrashekhar Meshram, 2020. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4561-4575, December.
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    Citations

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    Cited by:

    1. 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.
    2. Xinqin Gu & Li Yao & Lifeng Wu, 2023. "Prediction of Water Carbon Fluxes and Emission Causes in Rice Paddies Using Two Tree-Based Ensemble Algorithms," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    3. Long Zhao & Liwen Xing & Yuhang Wang & Ningbo Cui & Hanmi Zhou & Yi Shi & Sudan Chen & Xinbo Zhao & Zhe Li, 2023. "Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1207-1222, February.
    4. Dilip Kumar Roy & Tapash Kumar Sarkar & Sujit Kumar Biswas & Bithin Datta, 2023. "Generalized Daily Reference Evapotranspiration Models Based on a Hybrid Optimization Algorithm Tuned Fuzzy Tree Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 193-218, January.

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