Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration

Shima Amani (), Hossein Shafizadeh-Moghadam () and Saeid Morid ()
Additional contact information
Shima Amani: Tarbiat Modares University
Hossein Shafizadeh-Moghadam: Tarbiat Modares University
Saeid Morid: Tarbiat Modares University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 6, No 6, 1942 pages

Abstract: Abstract The current study evaluated the accuracy of four machine learning (ML) models and thirteen experimental methods calibrated to estimate reference evapotranspiration (ET0) in arid and semi-arid regions. Various scenarios were examined utilizing meteorological data and FAO56-PM as a benchmark. According to the results, the ML models outperformed the experimental methods on both daily and monthly scales. Among the ML models, artificial neural networks (ANNs), generalized additive model (GAM), random forest (RF), and support vector machine (SVM), respectively, demonstrated higher accuracy on a monthly scale, while ANNs, SVM, RF, and GAM exhibited greater accuracy on a daily scale. Notably, ANNs and SVM achieved high accuracy even with a limited number of variables. Conversely, RF showed improved accuracy with an increased number of variables. Comparing the ML and experimental models with equivalent inputs revealed that ANN with inputs similar to Valiantzas-1 performed better on a monthly scale, while SVM with inputs akin to Valiantzas-3 showed superior performance on a daily scale. Our findings suggest that average temperature, wind speed, and sunshine hours contribute significantly to the accuracy of ML models. Consequently, these ML models can serve as an alternative to the FAO56-PM method for estimating ET0.

Keywords: Reference evapotranspiration; FAO-56PM; Machine learning; Experimental models (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-023-03670-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:38:y:2024:i:6:d:10.1007_s11269-023-03670-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-023-03670-2

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().