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

Generalized multi-adaptive regression splines (MARS) and genetic expression programming (GEP)-based equations were developed to estimate Reference Evapotranspiration (ETo) in coastal regions. Following existing regression-based ETo retrieval 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 ETo accurately 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 ETo. The results also show that MARS can estimate ETo more accurately than GEP. The findings indicate that MARS1-MARS5 and GEP1-GEP5 improved ETo values compared with the corresponding traditional equations. Finally, sensitivity analyses were conducted to evaluate the impact of each input variable on ETo.