Artificial Intelligence for Sustainable Management of Groundwater Resources: New Developments, Challenges and Untapped Potentials
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrogeology".
Deadline for manuscript submissions: 15 May 2025 | Viewed by 1371
Special Issue Editor
Interests: coastal aquifers; sea level rise; subsidence; seawater intrusion; managed aquifer recharge; fractured aquifers; infiltration–recharge dynamics; groundwater pollution; surface water–groundwater interactions
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Special Issue Information
Dear Colleagues,
Artificial intelligence will play a critical role in international efforts to mitigate climate change’s impacts on the environment and preserve water resources.
Due to the combined effects of climate change and human activity in recent years, traditional methods in the fields of hydrology and water resources may not be sufficient to provide satisfactory results for practical engineering problems. Therefore, more focus has been placed on cutting-edge AI techniques in water resource management, leading to an increase in global research output. Specifically, AI has a great deal of potential for groundwater management in the future.
With the ability to provide accurate predictive modeling, real-time monitoring, and data integration, artificial intelligence (AI) is set to revolutionize groundwater management practices.
Several sophisticated artificial intelligence techniques, such as artificial neural networks, support vector machines, deep learning machines, Bayesian networks, Markov models, Kalman filters, chaos theory, and Gaussian process regression, have been successfully developed in recent decades to improve the understanding or simulation of the complex hydrodynamic processes found in nature. Generally speaking, by gaining important knowledge from a vast number of data samples, artificial intelligence techniques can accurately simulate the nonlinear relationship between the input and output variables.
Artificial intelligence (AI) techniques can be applied for the simultaneous prediction of groundwater heads, contaminant transport, and saltwater intrusion; the optimization of monitoring networks; and providing early warnings of critical conditions pertaining to the supply of drinking water and ecosystems that depend on groundwater. As AI techniques can transfer data spatially and temporally, they can extract complex relationships from existing data and offer significant advantages over established techniques. More sophisticated AI models can produce more accurate predictions of groundwater behavior, pinpoint areas susceptible to pollution and depletion, initiate preventive actions, and promote platforms for cooperation between local communities, policymakers, and scientists.
This Special Issue examines the multifaceted applications of AI in this field, breaking down its contributions, tackling related issues, and outlining its potential future applications.
It is still essential for resilient and sustainable groundwater management techniques to embrace AI's potential while tackling its obstacles. AI has the potential to develop a more resilient and sustainable groundwater management paradigm by overcoming obstacles through interdisciplinary collaboration and capacity-building. However, more research, policy integration, and technological innovation are required to harness the full power of AI to protect this invaluable resource.
Prof. Dr. Claudia Cherubini
Guest Editor
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Keywords
- artificial intelligence
- groundwater management
- groundwater heads
- contaminant transport
- saltwater intrusion
- optimization of monitoring networks
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