onr:"swepub:oai:DiVA.org:ltu-90689"
Search: onr:"swepub:oai:DiVA.org:ltu-90689" > Optimizing hyperpar...
- 1 of 1
- Previous record
- Next record
- To hitlist
Optimizing hyperparameters of deep hybrid learning for rainfall prediction: a case study of a Mediterranean basin
-
- Elbeltagi, Ahmed (author)
- Department of Agricultural Engineering, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt
-
- 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
-
- Bailek, Nadjem (author)
- Energies and Materials Research Laboratory, Department of Matter Sciences, Faculty of Sciences and Technology, University of Tamanrasset, 10034, Tamanrasset, Algeria
- show more...
- show less...
- 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.
- In: Arabian Journal of Geosciences. - : Springer. - 1866-7511 .- 1866-7538. ; 15
- Journal article (peer-reviewed)
Abstract
Subject headings
Close
- 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)
Find in a library
- Arabian Journal of Geosciences (Search for host publication in LIBRIS)
To the university's database
- 1 of 1
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Elbeltagi, Ahmed
- Zerouali, Bilel
- Bailek, Nadjem
- Bouchouicha, Kad ...
- Pande, Chaitanya
- Guimarães Santos ...
- show more...
- Towfiqul Islam, ...
- Al-Ansari, Nadhi ...
- El-kenawy, El-Sa ...
- show less...
- About the subject
-
- NATURAL SCIENCES
- NATURAL SCIENCES
- and Earth and Relate ...
- and Climate Research
- Articles in the publication
- Arabian Journal ...
- By the university
- Luleå University of Technology
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
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.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
