Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches-Reference-Cited by-同舟云学术

Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches

Published:2022-01-27 Issue:6 Volume:29 Page:3843-3859
ISSN:1134-3060
Container-title:Archives of Computational Methods in Engineering
language:en
Short-container-title:Arch Computat Methods Eng

Author:

Osman Ahmedbahaaaldin Ibrahem Ahmed,Ahmed Ali Najah^ORCID,Huang Yuk Feng,Kumar Pavitra,Birima Ahmed H.,Sherif Mohsen,Sefelnasr Ahmed,Ebraheemand Abdel Azim,El-Shafie Ahmed

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications

Link

https://link.springer.com/content/pdf/10.1007/s11831-022-09715-w.pdf

Reference82 articles.

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3. Amaranto A, Munoz-Arriola F, Solomatine DP, Corzo G (2019) A Spatially enhanced data-driven multimodel to improve semiseasonal groundwater forecasts in the high plains aquifer, USA. Water Resour Res 55:5941–5961. https://doi.org/10.1029/2018WR024301

4. Azamathulla HM (2013) A review on application of soft computing methods in water resources engineering, metaheuristics in water, geotechnical and transport engineering. Elsevier, New York, pp 27–41. https://doi.org/10.1016/b978-0-12-398296-4.00002-7

5. Bahmani R, Ouarda TBMJ (2020) Groundwater level modeling with hybrid artificial intelligence techniques. J Hydrol 595:125659. https://doi.org/10.1016/j.jhydrol.2020.125659

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