Rahman et al. (2025) Groundwater science in the age of AI: emerging paradigms and challenges

Identification

• Journal: Advances in Space Research
• Year: 2025
• Date: 2025-12-01
• Authors: Mahfuzur Rahman, Asif Raihan, Syed Masiur Rahman, Md Anuwer Hossain, Mohammed Benaafi, Isam H. Aljundi
• DOI: 10.1016/j.asr.2025.11.099

Research Groups

• Interdisciplinary Research Centre for Membranes & Water Security, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
• Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
• Department of Civil & Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
• Interdisciplinary Research Center for Construction & Building Materials, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
• Department of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh
• Geosciences Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
• Department of Chemical Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

Short Summary

This review synthesizes recent advances in artificial intelligence (AI) for sustainable groundwater management, demonstrating how emerging AI methods enhance forecasting accuracy, contaminant detection, and real-time decision support across key domains. It uniquely consolidates groundwater-specific applications, identifies research gaps, and introduces new paradigms to outline a future research agenda for transparent groundwater governance.

Objective

• To synthesize recent advances in artificial intelligence (AI) for sustainable groundwater management, focusing on predictive modeling, quality assessment, resource optimization, and integration with remote sensing and Internet of Things (IoT).
• To identify critical research gaps and introduce emerging paradigms such as explainable AI and digital twin frameworks in groundwater science.
• To outline a research agenda for data-driven, adaptive, and transparent groundwater governance under accelerating global water stress.

Study Configuration

• Spatial Scale: Global (review of general advancements applicable worldwide).
• Temporal Scale: Contemporary (synthesizes "recent advances" and discusses "emerging paradigms").

Methodology and Data

• Models used: Machine learning, deep learning, hybrid frameworks, explainable AI (XAI), digital twin frameworks (as discussed applications of AI).
• Data sources: Remote sensing, Internet of Things (IoT), and general hydrological data (implied for AI applications in groundwater management).

Main Results

• AI methods significantly enhance forecasting accuracy in groundwater predictive modeling.
• AI improves groundwater quality assessment, particularly in contaminant detection.
• AI optimizes groundwater resource management strategies.
• AI facilitates real-time decision support when integrated with remote sensing and IoT technologies.
• Emerging paradigms like explainable AI and digital twin frameworks offer new avenues for transparent and adaptive groundwater governance.

Contributions

• Uniquely consolidates groundwater-specific AI applications, differentiating it from previous broader reviews on AI in hydrology.
• Identifies critical research gaps within the application of AI to groundwater management.
• Introduces and discusses emerging paradigms such as explainable AI and digital twin frameworks in the context of groundwater science.
• Proposes a comprehensive research agenda for data-driven, adaptive, and transparent groundwater governance.

Funding

Not explicitly stated in the provided text.

Citation

@article{Rahman2025Groundwater,
  author = {Rahman, Mahfuzur and Raihan, Asif and Rahman, Syed Masiur and Hossain, Md Anuwer and Benaafi, Mohammed and Aljundi, Isam H.},
  title = {Groundwater science in the age of AI: emerging paradigms and challenges},
  journal = {Advances in Space Research},
  year = {2025},
  doi = {10.1016/j.asr.2025.11.099},
  url = {https://doi.org/10.1016/j.asr.2025.11.099}
}