SwePub
Sök i LIBRIS databas

  Utökad sökning

L773:1866 1955 OR L773:1866 1947
 

Sökning: L773:1866 1955 OR L773:1866 1947 > Optimizing hyperpar...

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

Elbeltagi, Ahmed (författare)
Department of Agricultural Engineering, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt
Zerouali, Bilel (författare)
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
Bailek, Nadjem (författare)
Energies and Materials Research Laboratory, Department of Matter Sciences, Faculty of Sciences and Technology, University of Tamanrasset, 10034, Tamanrasset, Algeria
visa fler...
Bouchouicha, Kada (författare)
Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS), Centre de Développement des Energies Renouvelables (CDER), 01000, Adrar, Algeria
Pande, Chaitanya (författare)
Department of Geology, Sant Gadge Baba Amravati University, Amravati, MS, 444602, India
Guimarães Santos, Celso Augusto (författare)
Department of Civil and Environmental Engineering, Federal University of Paraíba, João Pessoa, 58051-900, Brazil
Towfiqul Islam, Abueza Reza Md. (författare)
Department of Disaster Management, Begum Rokeya University, Rangpur, Bangladesh
Al-Ansari, Nadhir, 1947- (författare)
Luleå tekniska universitet,Geoteknologi
El-kenawy, El-Sayed M. (författare)
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
visa färre...
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
Engelska.
Ingår i: Arabian Journal of Geosciences. - : Springer. - 1866-7511 .- 1866-7538. ; 15
Relaterad länk:
https://urn.kb.se/re...
visa fler...
https://doi.org/10.1...
visa färre...
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Klimatforskning (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Climate Research (hsv//eng)

Nyckelord

Soil Mechanics
Geoteknik

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy