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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service prior to publication or during author revisions.

• water resource management
• surface hydrology
• subsurface energy
• forward modeling
• inverse modeling
• uncertainty quantification
• deep learning
• explainable AI