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<p>Shallow aquifers are important and viable water resources for agricultural and irrigation in rural areas. Significant research has been devoted to their characterization and detection especially in arid and semi-arid regions. However, the process is usually constrained by the inherent complexity of subsurface conditions and dynamic environmental factors. In recent years, artificial intelligence (AI) and machine learning (ML) have been applied to the prediction and mapping of shallow aquifers, offering faster and more accurate assessments of ground availability. Hence, this study systematically reviews the existing literature on the use of AI and ML in the detection and characterization of shallow aquifers. Furthermore, it highlights the major challenges associated with implementing these approaches and discusses relevant policy implications. Finally, the study identifies some research gaps and outlines directions for future work to enhance the integration of AI and ML in groundwater exploration.</p>

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Machine Learning and Data-Driven Approaches for Shallow Aquifers Management in Arid Regions: Current Advances, Research Gaps, and Opportunities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月2日

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  • Mohammed M. Bait-Suwailam,
  • Muhammet Deveci,
  • Mehtap Isik,
  • Ilgin Gokasar

摘要

Shallow aquifers are important and viable water resources for agricultural and irrigation in rural areas. Significant research has been devoted to their characterization and detection especially in arid and semi-arid regions. However, the process is usually constrained by the inherent complexity of subsurface conditions and dynamic environmental factors. In recent years, artificial intelligence (AI) and machine learning (ML) have been applied to the prediction and mapping of shallow aquifers, offering faster and more accurate assessments of ground availability. Hence, this study systematically reviews the existing literature on the use of AI and ML in the detection and characterization of shallow aquifers. Furthermore, it highlights the major challenges associated with implementing these approaches and discusses relevant policy implications. Finally, the study identifies some research gaps and outlines directions for future work to enhance the integration of AI and ML in groundwater exploration.