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Geocarto International Volume 37, 2022 - Issue 20
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Original Articles

A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers

Qasim Khana Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, United Arab EmiratesView further author information
,
Muhammad Usman Liaqatb Department of Civil, Environmental, Architectural and Engineering, University of Brescia, ItalyView further author information
&
Mohamed Mostafa Mohameda Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, United Arab Emirates;c National Water Center, United Arab Emirates University, Al Ain, United Arab EmiratesCorrespondence[email protected]
View further author information
Pages 5832-5850 | Received 11 Feb 2021, Accepted 05 Apr 2021, Published online: 01 Jun 2021

Citations (3)

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Read on this site (1)

Mohamed Haythem Msaddek, Yahya Moumni, Alaeddine Ayari, Moufida El May & Ismail Chenini. (2022) Artificial intelligence modelling framework for mapping groundwater vulnerability of fractured aquifer. Geocarto International 37:25, pages 10480-10510.
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Articles from other publishers (2)

Tanya J. Gallegos, Victoria G. Stengel, Katie Walton-Day, Johanna Blake, Andrew Teeple, Delbert Humberson, Steven Cahan, Douglas B. Yager & Kent D. Becher. (2022) A Novel Method for Conducting a Geoenvironmental Assessment of Undiscovered ISR-Amenable Uranium Resources: Proof-of-Concept in the Texas Coastal Plain. Minerals 12:6, pages 747.
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Qasim Khan, Dalal Alshamsi, Saber Hussein & Mohamed Mohamed. (2021) Understanding the activity of Radon-222 in a sand dune aquifer of an arid region through the application of machine learning. Groundwater for Sustainable Development 15, pages 100667.
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