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Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply

Environmental Science and Pollution Research volume 28, pages 6520–6532 (2021)Cite this article

Abstract

Adopting methodologies utilizing exogenous data from ancillary stations for determining crop water requirement is a suitable approach to exempt local shortcomings due to the lack of meteorological data/stations. Meanwhile, soft computing techniques might be suitable tools to be used with such data management scenarios. The present paper aimed at evaluating the generalizability of the gene expression programming (GEP) technique for estimating reference evapotranspiration (ET0) through cross-station assessment and exogenous data supply, using data from Turkey and Iran. The GEP-based models were established and learnt using data from 10 stations in Turkey, and then the developed models were tested (validated) in 18 stations of Iran with considerable latitude differences. Different time periods (beginning and the end of time series) were selected for the training and testing stations so that there was no overlap among the dates of the events in both the groups. A comparison was also performed between the GEP models and the corresponding commonly used empirical equations. The obtained results revealed that the generalized GEP models presented promising outcomes in simulating daily ET0 values when they were trained and tested in quite distant stations with different chronological periods of the applied parameters. The performance accuracy of the empirical equations calibrated using exogenous data was reduced in comparison with their original (non-calibrated) versions. Further, although the generalization ability of the GEP models was reduced when the climatic context of the training-testing stations was different, the overall performance accuracy of those models was higher than those of the commonly used classic empirical equations.

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Affiliations

  1. Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Mohammad Hossein Kazemi, Abolfazl Majnooni-Heris & Jalal Shiri

  2. Civil Engineering Department, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

  3. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Ozgur Kisi

  4. Center of Excellence in Hydro-informatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Jalal Shiri

Authors
  1. Mohammad Hossein Kazemi
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  2. Abolfazl Majnooni-Heris
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  3. Ozgur Kisi
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  4. Jalal Shiri
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Contributions

M.H.K. made the investigation, simulation, and validation phases. A.M. prepared the necessary resources and software and had contribution in modeling phase. O.K. helped with preparing some necessary data and had contribution in conceptualization and writing the paper. J.S. made the fundamental concept of the study and defined the methodology. He had contribution in writing the paper (both original and revised versions). All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jalal Shiri.

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The datasets analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

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Kazemi, M.H., Majnooni-Heris, A., Kisi, O. et al. Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply. Environ Sci Pollut Res 28, 6520–6532 (2021). https://doi.org/10.1007/s11356-020-10916-8

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  • DOI: https://doi.org/10.1007/s11356-020-10916-8

Keywords

  • Data management
  • Data supplanting
  • Extrapolation
  • Generalized models