Predicting reference evapotranspiration based on hydro-climatic variables: comparison of different machine learning models

ABSTRACT

This paper aimed to estimate the reference evapotranspiration (ET₀) due to some limitations of the Food and Agriculture Organization-56 Penman-Monteith (FAO 56-PM) approach by using five alternative machine learning models. The study makes an important contribution to the ET₀ estimation success for of the ET₀ of 12 stations with variable climate characteristics in the Central Anatolian Region (CAR). The performances of the models were compared with the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) metrics that are frequently cited in the literature, and also with the performance index (PI). Long short-term memory (LSTM), artificial neural networks (ANN), and multivariate adaptive regression splines (MARS) models provided the best performance in eight, three, and one stations, respectively. The R², MAE, RMSE, and PI values of the selected models from each station vary in the range of 0.987-0.999, 1.948-4.567, 2.671-6.659, and 1.544-4.018, respectively.

KEYWORDS:

• reference evapotranspiration (ET₀)
• FAO-56 Penman-Monteith (FAO 56-PM) approach
• machine learning approaches
• performance measures

Editor S. Archfield; Associate Editor O. Kisi

Editor S. Archfield; Associate Editor O. Kisi

Acknowledgements

We give many thanks to associate editor Ozgur Kisi, and other anonymous reviewers for their valuable feedback.

Disclosure statement

No potential conflict of interest was reported by the authors.