Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones
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Published:2024-06
Issue:2
Volume:27
Page:178-191
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ISSN:1110-9823
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Container-title:The Egyptian Journal of Remote Sensing and Space Sciences
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language:en
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Short-container-title:The Egyptian Journal of Remote Sensing and Space Sciences
Author:
Al-Ruzouq Rami,
Shanableh AbdallahORCID,
Jena Ratiranjan,
Mukherjee Sunanda,
Ali Khalil Mohamad,
Gibril Mohamed Barakat A.,
Pradhan Biswajeet,
Atalla Hammouri Nezar
Funder
University of Sharjah
Reference54 articles.
1. Video description: A survey of methods, datasets, and evaluation metrics;Aafaq;ACM Comput. Surv.,2019
2. GIS-based multi-criteria analysis for identification of potential groundwater recharge zones-a case study from Ponnaniyaru watershed, Tamil Nadu, India;Abijith;HydroResearch,2020
3. Remote sensing and GIS based groundwater potential & recharge zones mapping using multi-criteria decision making technique;Agarwal;Water Resour. Manag.,2016
4. The Gulf Cooperation Council security and the Arab order: Relations in question;Al-Busaid,1995
5. Aller, L., Thornhill, J., 1987.DRASTIC: a standardized system for evaluating ground water pollution potential using hydrogeologic settings. Robert S. Kerr Environmental Research Laboratory, Office of Research and Development, US Environmental Protection Agency.
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