Artificial Intelligence (AI) Solutions for Hydrogeological Challenges
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrogeology".
Deadline for manuscript submissions: 10 March 2026 | Viewed by 2
Special Issue Editors
Interests: groundwater management; karst aquifers; coastal aquifers; hydrogeochemistry; groundwater monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: water isotopes; groundwater; tracers; water management; aquifer recharge; carbonate aquifers; water budget; hydrogeological conceptual models; groundwater hydrodynamics; data-driven methods
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and machine learning (ML) are gaining ground in every scientific field and in the daily lives of all people. This trend has been driven in part by the widespread adoption of sensors and monitoring technologies, which over the past 20–30 years have enabled the collection and storage of vast amounts of data. In the field of groundwater management, the most far-sighted water utilities and managers have obtained, at a low cost, historical series of data (ranging from water quality parameters to piezometric levels and spring flow rates). These are now proving extremely useful in training neural network models for simulating and predicting with excellent accuracy many of the hydrogeological phenomena that the same physical models often struggle to represent and understand.
This Special Issue will collect the most recent and exciting findings regarding AI and intelligent systems more generally to support hydrogeology, proposing new methods and models and solving challenges related to climate change, pollution and any other groundwater-related issue.
Dr. Francesco Maria De Filippi
Dr. Davide Fronzi
Dr. Alessandro Galdelli
Guest Editors
Manuscript Submission Information
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Keywords
- hydrogeology
- springs
- groundwater availability
- wells
- aquifer recharge
- machine learning
- artificial intelligence
- neural networks
- predictive modeling
- intelligent systems
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