TY - JOUR
T1 - Estimation of daily reference evapotranspiration from limited climatic variables in coastal regions
AU - Vosoughifar, Hamidreza
AU - Khoshkam, Helaleh
AU - Bateni, Sayed M.
AU - Jun, Changhyun
AU - Xu, Tongren
AU - Band, Shahab S.
AU - Neale, Christopher M.U.
N1 - Publisher Copyright:
© 2022 IAHS.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - coastal regions
KW - genetic expression programming (GEP)
KW - multi-adaptive regression splines (MARS)
KW - reference evapotranspiration
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U2 - 10.1080/02626667.2022.2142473
DO - 10.1080/02626667.2022.2142473
M3 - Article
AN - SCOPUS:85144052721
SN - 0262-6667
VL - 68
SP - 91
EP - 107
JO - Hydrological Sciences Journal
JF - Hydrological Sciences Journal
IS - 1
ER -