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.

Automated summary

This study developed MARS and GEP models for estimating ETo in coastal regions and evaluated their performances. The results showed that the generalized MARS1-MARS5 and GEP1-GEP5 models accurately estimated ETo in regions other than their training region, with MARS1 and GEP1 performing the best. The study also found that MARS outperformed GEP in estimating ETo and improved the estimation of ETo.