Modeling of meteorological, agricultural, and hydrological droughts in semi-arid environments with various machine learning and discrete wavelet transform-Reference-Cited by-同舟云学术

Modeling of meteorological, agricultural, and hydrological droughts in semi-arid environments with various machine learning and discrete wavelet transform

Published:2023-08-18 Issue:1-2 Volume:154 Page:413-451
ISSN:0177-798X
Container-title:Theoretical and Applied Climatology
language:en
Short-container-title:Theor Appl Climatol

Author:

Achite Mohammed^ORCID,Katipoglu Okan Mert^ORCID,Şenocak Serkan^ORCID,Elshaboury Nehal^ORCID,Bazrafshan Ommolbanin^ORCID,Dalkılıç Hüseyin Yıldırım^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Atmospheric Science

Link

https://link.springer.com/content/pdf/10.1007/s00704-023-04564-4.pdf

Reference69 articles.

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2. Achite M, Banadkooki FB, Ehteram M, Bouharira A, Ahmed AN, Elshafie A (2022a) Exploring bayesian model averaging with multiple ANNs for meteorological drought forecasts. Stoch Environ Res Risk Assess 1-26. https://doi.org/10.1007/s00477-021-02150-6

3. Achite M, Jehanzaib M, Elshaboury N, Kim TW (2022b) Evaluation of machine learning techniques for hydrological drought modeling: a case study of the Wadi Ouahrane Basin in Algeria. Water 14(3):431. https://doi.org/10.3390/w14030431

4. Aghelpour P, Kisi O, Varshavian V (2021) Multivariate drought forecasting in short-and long-term horizons using MSPI and data-driven approaches. J Hydrol Eng 26(4):04021006

5. Ahmadpour H, Bazrafshan O, Rafiei-Sardooi E, Zamani H, Panagopoulos T (2021) Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection. Sustainability 13(18):10110

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