ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Data Fusion and Assimilation
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1622360
This article is part of the Research TopicAdvanced Geospatial Data Analytics for Environmental Sustainability: Current Practices and Future ProspectsView all 6 articles
Artificial Intelligence for Groundwater Recharge Prediction in an Arid Region : Application of Tabular Deep Learning models in the Feija Basin, Morocco
Provisionally accepted
Abdessamad Elmotawakkil1
Adil Moumaneb1
Assia Zahi2Abdelkhalik Sadiki1Jamal Al Karkouri1Mouhcine Batchi1
SURAJ KUMAR BHAGAT2*Tiysha Tiyasha3Nourddine Enneya1- 1University of Ibn Tofail, Kenitra, Morocco
- 2Marwadi University, Rajkot, India
- 3Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Groundwater recharge mapping in arid and semi-arid regions is vital for sustainable water resource management, particularly in hydro-climatically stressed zones such as the Feija Basin in southeastern Morocco. This region, characterized by shallow phreatic aquifers, limited and irregular rainfall, and intensive groundwater exploitation for high-demand crops like watermelon, faces escalating depletion risks. Recent anomalous hydrological conditions, including exceptional rainfall during the 2024-2025 season, have underscored both the vulnerability and opportunity inherent in these landscapes, emphasizing the need for data-driven approaches that support proactive recharge strategies, enhance aquifer replenishment, and mitigate potential flood impacts
Keywords: Machine Learing, Groundwater research, Arid region, TabNet, Morroco
Received: 03 May 2025; Accepted: 29 Jul 2025.
Copyright: © 2025 Elmotawakkil, Moumaneb, Zahi, Sadiki, Karkouri, Batchi, BHAGAT, Tiyasha and Enneya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: SURAJ KUMAR BHAGAT, Marwadi University, Rajkot, India
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