Performance of machine learning methods for modeling reservoir management based on irregular daily data sets: a case study of Zit Emba dam
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01160-y.pdf
Reference91 articles.
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2. Ahmed AN, Othman FB, Afan HA, Ibrahim RK, Fai CM, Hossain MS, Ethteram M, Elshafie A (2019) Machine learning methods for better water quality prediction. J Hydrol 578:124084. https://doi.org/10.1016/j.jhydrol.2019.124084
3. Ahn S, Sheng Z (2021) Assessment of water availability and scarcity based on hydrologic components in an irrigated agricultural watershed using SWAT. JAWRA Journal of the American Water Resources Association 57(1):186–203. https://doi.org/10.1111/1752-1688.12888
4. Allawi MF, Jaafar O, Hamzah FM, El-Shafie A (2019a) Novel reservoir system simulation procedure for gap minimization between water supply and demand. J Clean Prod 206:928–943. https://doi.org/10.1016/j.jclepro.2018.09.237
5. Allawi MF, Jaafar O, Hamzah FM, Koting S, Mohd NS, El-Shafie A (2019b) Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance. Knowledge Based Systems 163:907–926. https://doi.org/10.1016/j.knosys.2018.10.013
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