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

Mustaq Shaikh 1, * and Farjana Birajdar 2

1 Groundwater Surveys and Development Agency, Solapur, GoM, India.
2 School of Earth Sciences, Punyashlok Ahilyadevi Holkar Solapur University, India.
 
Review
International Journal of Science and Research Archive, 2024, 11(01), 502–512.
Article DOI: 10.30574/ijsra.2024.11.1.0105
Publication history: 
 Received on 14 December 2023; revised on 20 January 2024; accepted on 22 January 2024
 
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.
 
Keywords: 
Groundwater management; Artificial intelligence; Predictive modeling; Real-time monitoring; Decision support systems
 
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