An explicit neural network formulation for evapotranspiration

Ali Aytek*, Aytac Guven, M. Ishak Yuce, Hazullah Aksoy

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)


An explicit neural network formulation (ENNF) is developed for estimating reference evapotranspiration (ET0) using daily meteorological variables obtained from the California Irrigation Management Information System (CIMIS) database. First, the ENNF is trained and tested using the CIMIS database, and then compared with five conventional ET0 models, as well as the multiple linear regression method. Statistics such as average, standard deviation, minimum and maximum values, and criteria such as mean square error and determination coefficient are used to measure the performance of the ENNF. Daily atmospheric data of four climatic stations in central California are taken into consideration in the model development and those of three other stations are used for comparison purposes. The meteorological variables employed in the formulation are solar radiation, air temperature, relative humidity and wind speed. It is concluded from the results that ENNF offers an alternative ET0 formulation, but that the gain in skill is marginal compared with simpler linear techniques. However, this finding needs to be tested using sites drawn from a wider range of climate regimes.

Original languageEnglish
Pages (from-to)893-904
Number of pages12
JournalHydrological Sciences Journal
Issue number4
Publication statusPublished - Aug 2008


Acknowledgements The authors wish to thank the reviewers for their constructive and fruitful comments, which helped to improve the manuscript. The encouragement of Prof. D. Koutsoyiannis is also appreciated. Sincere thanks are also due to Miss Ebru Eris, Hydraulics Division, Civil Engineering Department, Istanbul Technical University, for her help in the study. Finally, the authors would like to thank the Research Foundation of Gaziantep University for the support provided for the study.

FundersFunder number
Gaziantep Üniversitesi


    • Artificial neural networks
    • CIMIS database
    • Evapotranspiration
    • Penman-Monteith equation


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