Artificial intelligence in groundwater management: Innovations, challenges, and future prospects

Abstract

The integration of Artificial Intelligence (AI) in groundwater management is a transformative stage, characterized by innovation and challenges. This research paper explores the multilayered application of AI in this field, dividing its contributions, addressing its associated challenges, and revealing the prospects of future potential. AI-driven innovations are designed to revolutionize groundwater management, providing precise predictive modeling, real-time monitoring, and data integration. However, these innovations face challenges such as interpretability issues, specialized technical expertise requirements, and limited data quality and quantity for effective AI model performance. In the future, AI holds significant promise in groundwater management. Advanced AI models can yield improved predictions of groundwater behavior, identify vulnerable areas prone to pollution and depletion, prompt proactive interventions, and foster collaborative platforms among scientists, policymakers, and local communities. Collaborative platforms driven by AI offer potential for synergistic engagement among scientists, policymakers, and local communities, collectively guiding groundwater resource management. Embracing AI's potential while addressing its challenges remains pivotal for sustainable and resilient groundwater management practices. By embracing AI's potential while addressing its challenges, the landscape of groundwater resource management will continue to evolve.