Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm

Yash Agrawal (), Manoranjan Kumar (), Supriya Ananthakrishnan () and Gopalakrishnan Kumarapuram ()
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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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 3, No 15, 1025-1042

Abstract: 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.

Keywords: Ensembled machine learning; Reference evapotranspiration; Decision tree; XGBoost (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11269-022-03067-7

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