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August 4, 2025WaterOpen Access

Integrating GIS, Remote Sensing, and Machine Learning to Optimize Sustainable Groundwater Recharge in Arid Mediterranean Landscapes: A Case Study from the Middle Draa Valley, Morocco

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Authors

AMAdil MoumaneAEAbdessamad ElmotawakkilMHMd. Mahmudul Hasan

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Overview

Machine learning techniques improve groundwater recharge mapping in the Middle Draa Valley, suggesting enhanced irrigation and aquifer conservation strategies.

Key Points

  • Integrating machine learning and GIS techniques maps suitable groundwater recharge zones in rural Morocco.
  • LightGBM achieved an accuracy of 90%, providing the best predictive performance for recharge zone identification.
  • Soil permeability, elevation, and stream proximity were key factors influencing recharge mapping results.
  • The study offers a transferable framework supporting sustainable groundwater management and climate-resilient practices.

Cite This Study

Moumane et al. (2025) studied this question.

synapsesocial.com/papers/689522009f4f1c896c4290cdhttps://doi.org/10.3390/w17152336
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