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Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions

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Abstract

Evapotranspiration is an important quantity required in many applications, such as hydrology and agricultural and irrigation planning. Reference evapotranspiration is particularly important, and the prediction of its variations is beneficial for analyzing the needs and management of water resources. In this paper, we explore the predictive ability of hybrid ensemble learning to predict daily reference evapotranspiration (RET) under the semi-arid climate by using meteorological datasets at 12 locations in the Andalusia province in southern Spain. The datasets comprise mean, maximum, and minimum air temperatures and mean relative humidity and mean wind speed. A new modified variant of the grey wolf optimizer, named the PRSFGWO algorithm, is proposed to maximize the ensemble learning’s prediction accuracy through optimal weight tuning and evaluate the proposed model’s capacity when the climate data is limited. The performance of the proposed approach, based on weighted ensemble learning, is compared with various algorithms commonly adopted in relevant studies. A diverse set of statistical measurements alongside ANOVA tests was used to evaluate the predictive performance of the prediction models. The proposed model showed high-accuracy statistics, with relative root mean errors lower than 0.999% and a minimum R2 of 0.99. The model inputs were also reduced from six variables to only two for cost-effective predictions of daily RET. This shows that the PRSFGWO algorithm is a good RET prediction model for the semi-arid climate region in southern Spain. The results obtained from this research are very promising compared with existing models in the literature.

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Acknowledgements

The authors acknowledge the Agroclimatic-Information-Network of Andalusia (RIA) in providing the most used data.

Funding

This work was supported by the Portuguese Foundation for Science and Technology (FCT) through PTDC/CTA-OHR/30561/2017 (WinTherface).

Author information

Authors and Affiliations

  1. Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansour, 35111, Egypt

    El-Sayed M. El-kenawy

  2. Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt

    El-Sayed M. El-kenawy & Marwa Eid

  3. Vegetal Chemistry-Water-Energy Laboratory, Faculty of Civil Engineering and Architecture, Department of Hydraulic, Hassiba Benbouali University of Chlef, B.P. 78C, Ouled Fares, 02180, Chlef, Algeria

    Bilel Zerouali

  4. Energies and Materials Research Laboratory, Department of Matter Sciences, Faculty of Sciences and Technology, University of Tamanghasset, Tamanghasset, Algeria

    Nadjem Bailek

  5. Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS), Centre de Développement Des Energies Renouvelables (CDER), 01000, Adrar, Algeria

    Kada Bouchouich

  6. Mechanical Power Engineering Department, Faculty of Engineering, Cairo University, Giza, Giza, 12613, Egypt

    Muhammed A. Hassan

  7. Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt

    Javier Almorox

  8. CERIS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Alban Kuriqi

  9. Civil Engineering Department, University for Business and Technology, Pristina, Kosovo

    Alban Kuriqi

  10. Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt

    Abdelhameed Ibrahim‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

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  1. El-Sayed M. El-kenawy
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  2. Bilel Zerouali
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  9. Abdelhameed Ibrahim‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
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Contributions

EME: investigation, conceptualization, data curation, visualization, formal analysis, writing — original draft, writing—review and editing.

BZ: conceptualization, data curation, visualization, formal analysis, writing — original draft, writing — review and editing, validation.

NB: conceptualization, data curation, visualization, formal analysis, writing — original draft, writing — review and editing, validation.

KB: conceptualization, data curation, writing—original draft, writing — review and editing.

MAH: methodology, formal analysis, writing—original draft, writing — review and editing, validation.

JA: conceptualization, writing—original draft, writing — review and editing.

AK: conceptualization, writing—original draft, writing — review and editing.

ME: resources, writing — original draft, writing — review and editing.

AI: resources, software, writing — original draft, validation.

Corresponding author

Correspondence to Nadjem Bailek.

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El-kenawy, ES.M., Zerouali, B., Bailek, N. et al. Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions. Environ Sci Pollut Res 29, 81279–81299 (2022). https://doi.org/10.1007/s11356-022-21410-8

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