A Review on Drought Index Forecasting and Their Modelling Approaches
Author:
Funder
Fundamental Research Grant Scheme
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11831-022-09828-2.pdf
Reference49 articles.
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2. Achite M, Jehanzaib M, Elshaboury N, Kim TW (2022) Evaluation of machine learning techniques for hydrological drought modeling: a case study of the Wadi Ouahrane Basin in Algeria. Water 14(14):431. https://doi.org/10.3390/W14030431
3. 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
4. Ali Z, Hussain I, Nazeer A, Faisal M, Ismail M, Qamar S, Grzegorczyk M, Zahid FM, Ni G (2020) Measuring and restructuring the risk in forecasting drought classes: an application of weighted Markov chain based model for standardised precipitation evapotranspiration index (SPEI) at one-month time scale. Tellus A Dyn Meteorol Oceanogr 72:1–10. https://doi.org/10.1080/16000870.2020.1840209
5. Almikaeel W, Čubanová L, Šoltész A (2022) Hydrological drought forecasting using machine learning—Gidra River case study. Water (Switzerland) 14:387. https://doi.org/10.3390/w14030387
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