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Optimizing hyperpar...
Optimizing hyperparameters of deep hybrid learning for rainfall prediction: a case study of a Mediterranean basin
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- Elbeltagi, Ahmed (author)
- Department of Agricultural Engineering, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt
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- Zerouali, Bilel (author)
- 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
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- Bailek, Nadjem (author)
- Energies and Materials Research Laboratory, Department of Matter Sciences, Faculty of Sciences and Technology, University of Tamanrasset, 10034, Tamanrasset, Algeria
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- Bouchouicha, Kada (author)
- Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS), Centre de Développement des Energies Renouvelables (CDER), 01000, Adrar, Algeria
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- Pande, Chaitanya (author)
- Department of Geology, Sant Gadge Baba Amravati University, Amravati, MS, 444602, India
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- Guimarães Santos, Celso Augusto (author)
- Department of Civil and Environmental Engineering, Federal University of Paraíba, João Pessoa, 58051-900, Brazil
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- Towfiqul Islam, Abueza Reza Md. (author)
- Department of Disaster Management, Begum Rokeya University, Rangpur, Bangladesh
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- Al-Ansari, Nadhir, 1947- (author)
- Luleå tekniska universitet,Geoteknologi
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- El-kenawy, El-Sayed M. (author)
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt; Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt
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Department of Agricultural Engineering, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt Vegetal Chemistry-Water-Energy Laboratory, Faculty of Civil Engineering and Architecture, Department of Hydraulic, Hassiba Benbouali University of Chlef, BP. 78C, Ouled Fares, 02180, Chlef, Algeria (creator_code:org_t)
- 2022-05-05
- 2022
- English.
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In: Arabian Journal of Geosciences. - : Springer. - 1866-7511 .- 1866-7538. ; 15
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Klimatforskning (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Climate Research (hsv//eng)
Keyword
- Soil Mechanics
- Geoteknik
Publication and Content Type
- ref (subject category)
- art (subject category)
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