AI and Machine Learning in Hydrogeology

Dear Colleagues,

This Special Issue focuses on the transformative role of artificial intelligence (AI) and machine learning (ML) in hydrogeology and hydrological systems, spanning both surface and subsurface domains. We welcome contributions that address applications in groundwater modeling, surface water management, contaminant transport, subsurface energy systems, oil and gas recovery, geological carbon storage, geothermal energy, and hydrogen storage. Emphasis is placed not only on traditional ML techniques but also on emerging AI paradigms such as generative models (e.g., diffusion models), large language models (LLMs), and graph neural networks.

The issue covers a range of topics including forward and inverse modeling, uncertainty quantification, real-time decision support, and deep learning architectures designed for hydrological processes. Particular attention is given to challenges in multi-source data integration, model interpretability, and physics-informed learning. Through this interdisciplinary collection, we aim to demonstrate how cutting-edge AI tools can improve predictive capabilities, support sustainable resource management, and deepen our understanding of complex environmental systems.

Dr. Ming Fan
Dr. Chaojie Cheng
Dr. Linqi Zhu
Guest Editors

Manuscript Submission Information

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