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Optimizing hyperparameters of deep hybrid learning for rainfall prediction: a case study of a Mediterranean basin

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Abstract

Predicting rainfall amount is essential in water resources planning and for managing structures, especially those against floods and long-term drought establishment. Machine learning techniques can produce good results using a minimum dataset requirement, making it a leader among the prediction algorithms. This work develops a hybrid learning model for monthly rainfall prediction at four geographical locations representing Mediterranean basins in Northern Algeria and desert areas in Egypt. The study proposes an adaptive dynamic-based hyperparameter optimization algorithm to improve the accuracy of hybrid deep learning models. The proposed model provided a good fit, based on the obtained Nash-Sutcliffe efficiency index (NSE ≈ 0.90) with a high correlation coefficient of R ≈ 0.96, providing improvements of up to 62% in the RMSE. The proposed method proved to be an encouraging and promising tool to simulate water cycle components for better water resources management and protection.

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Data availability

The data that support the findings of this study are owned by the Algerian Meteorological Office (ONM) of Algeria.

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Acknowledgments

The authors are deeply grateful to the Algerian Meteorological Office for providing the data used in the present manuscript.

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Authors and Affiliations

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

    Ahmed Elbeltagi

  2. 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

  3. Energies and Materials Research Laboratory, Department of Matter Sciences, Faculty of Sciences and Technology, University of Tamanrasset, 10034, Tamanrasset, Algeria

    Nadjem Bailek

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

    Kada Bouchouicha

  5. Department of Geology, Sant Gadge Baba Amravati University, Amravati, MS, 444602, India

    Chaitanya Pande

  6. Department of Civil and Environmental Engineering, Federal University of Paraíba, João Pessoa, 58051-900, Brazil

    Celso Augusto Guimarães Santos

  7. Department of Disaster Management, Begum Rokeya University, Rangpur, Bangladesh

    Abueza Reza Md. Towfiqul Islam

  8. Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden

    Nadhir Al-Ansari

  9. Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt

    El-Sayed M. El-kenawy

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

    El-Sayed M. El-kenawy

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  1. Ahmed Elbeltagi
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  2. Bilel Zerouali
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  3. Nadjem Bailek
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  4. Kada Bouchouicha
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  5. Chaitanya Pande
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  6. Celso Augusto Guimarães Santos
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  7. Abueza Reza Md. Towfiqul Islam
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  8. Nadhir Al-Ansari
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  9. El-Sayed M. El-kenawy
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Correspondence to Nadjem Bailek or Celso Augusto Guimarães Santos.

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Elbeltagi, A., Zerouali, B., Bailek, N. et al. Optimizing hyperparameters of deep hybrid learning for rainfall prediction: a case study of a Mediterranean basin. Arab J Geosci 15, 933 (2022). https://doi.org/10.1007/s12517-022-10098-2

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