Predicting hydrological droughts using ERA 5 reanalysis data and wavelet-based soft computing techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth-Surface Processes,Geology,Pollution,Soil Science,Water Science and Technology,Environmental Chemistry,Global and Planetary Change
Link
https://link.springer.com/content/pdf/10.1007/s12665-023-11280-9.pdf
Reference58 articles.
1. Achite M, Jehanzaib M, Elshaboury N, Kim T-W (2022) Evaluation of machine learning techniques for hydrological drought modeling: a case study of the Wadi Ouahrane basin in Algeria. Water 14:431. https://doi.org/10.3390/w14030431
2. Achite M, Katipoğlu OM, Jehanzaib M, Elshaboury N, Kartal V, Ali S (2023a) Hydrological drought prediction based on hybrid extreme learning machine: Wadi Mina Basin Case Study, Algeria. Atmosphere 14(9):1447. https://doi.org/10.3390/atmos14091447
3. Achite M, Katipoglu OM, Şenocak S, Elshaboury N, Bazrafshan O, Dalkılıç HY (2023b) Modeling of meteorological, agricultural, and hydrological droughts in semi-arid environments with various machine learning and discrete wavelet transform. Theor Appl Climatol. https://doi.org/10.1007/s00704-023-04564-4
4. Aghelpour P, Bahrami-Pichaghchi H, Varshavian V (2021) Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran. Stochastic Environ Res Risk Assess 35:1615–1635. https://doi.org/10.1007/s00477-020-01949-z
5. Ahmadi F, Mehdizadeh S, Mohammadi B (2021) Development of bio-inspired-and wavelet-based hybrid models for reconnaissance drought index modeling. Water Resour Manag 35:4127–4147. https://doi.org/10.1007/s11269-021-02934-z
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