Reference:Reference-024111: Difference between revisions

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|BibType=journalArticle
|Title=Integrating MODFLOW and machine learning for detecting optimum groundwater abstraction considering sustainable drawdown and climate changes
|Year=2024-06
|Journal=Journal of Hydrology
|Booktitle=None
|Volume=637
|Pages=131428
|URL=https://linkinghub.elsevier.com/retrieve/pii/S0022169424008230
|DOI=10.1016/j.jhydrol.2024.131428
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Revision as of 06:55, 5 September 2024



Author(s) Makhlouf, Ahmed; El-Rawy, Mustafa; Kanae, Shinjiro; Ibrahim, Mona G.; Sharaan, Mahmoud;
BibType journalArticle
Title Integrating MODFLOW and machine learning for detecting optimum groundwater abstraction considering sustainable drawdown and climate changes
Editors
Year 2024-06
Journal Journal of Hydrology
Booktitle None
Volume 637
Pages 131428
URL https://linkinghub.elsevier.com/retrieve/pii/S0022169424008230
DOI 10.1016/j.jhydrol.2024.131428
ISBN
Note Auto downloaded ref at: 2024-09-04


 
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Nr of citations 0
Sort of publication a module application description
Sort of model publication a single module
Is the CSDMS HPC used No
If HPC is used, for what project was it?:
Associated simulation movie if any:


Model(s) discussed: MODFLOW