Vytautas Samalavičius, Ilya Zaslavsky

Artificial intelligence in hydrogeology: applications and an open, reproducible machine learning course

Santrauka Artificial intelligence (AI) is increasingly used in hydrogeological analysis for forecasting groundwater levels and properties, discovering hydrochemical facies and anomalies, and modelling spatio-temporal signals. Current practice emphasizes spatio-temporal validation, interpretability, and uncertainty quantification, while generative AI (GenAI) is emerging to accelerate data curation, documentation, augmentation, and code prototyping. This report summarizes representative applications of AI and machine learning (ML) in hydrogeology and describes an open, reproducible course developed for university students and professional hydrogeologists. This continuously updated online course is one of the outcomes of the GRANDE-U “Groundwater Resilience Assessment through Integrated Data Exploration for Ukraine” project. It covers Python fundamentals, environment setup, data engineering, and documented case studies in time-series and spatio-temporal modelling. Application examples are drawn from recent teaching materials and GRANDE-U and related studies and include predicting terrestrial water-storage anomalies, groundwater-level forecasting, isotope estimation from routine chemistry, risk or prospectivity mapping, and automated lineament/facies mapping.

Doi https://doi.org/10.5200/baltica.2026.1.4

Raktažodžiai machine learning; groundwater; spatial data; temporal data

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