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Generalized multi-adaptive regression splines (MARS) and genetic expression programming (GEP)-based equations were developed to estimate Reference Evapotranspiration ( ET o) in coastal regions. Following existing regression-based  ET oretrieval equations, five climatic data configurations were used to train, validate, and test the MARS and GEP models (hereafter called MARS1–MARS5 and GEP1–GEP5). The performances of the MARS and GEP models with each of the five input configurations were assessed. The generalized MARS1–MARS5 and GEP1–GEP5 models could estimate  ET oaccurately in regions other than their training region. In addition, MARS1 performed better than MARS2–MARS5. Similarly, GEP1 outperformed GEP2–GEP5, implying that input configuration 1 contains the most important information about  ET o. The results also show that MARS can estimate  ET omore accurately than GEP. The findings indicate that MARS1–MARS5 and GEP1–GEP5 improved  ET ovalues compared with the corresponding traditional equations. Finally, sensitivity analyses were conducted to evaluate the impact of each input variable on  ET o.

Estimation of daily reference evapotranspiration from limited climatic variables in coastal regions