AI and Machine Learning in Hydrogeology

A special issue of Geosciences (ISSN 2076-3263).

Deadline for manuscript submissions: 28 February 2026 | Viewed by 108

Special Issue Editors

Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Interests: subsurface energy; machine learning; explainable AI; uncertainty quantification; inverse modeling; scientific computing
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Guest Editor
KIT—Karlsruhe Institute of Technology, Adenauerring 20a, 76135 Karlsruhe, Germany
Interests: reactive transport; fluid–rock–(microbe) interactions; rock mechanics; geothermal energy; underground hydrogen storage

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Guest Editor
Department of Earth Science & Engineering, Faculty of Engineering, Imperial College London, London, UK
Interests: AI for geoscience; subsurface energy; flow in porous media; petrophysics; underground hydrogen/CO2 storage; formation evaluation

Special Issue Information

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

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Keywords

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

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Published Papers (1 paper)

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Research

17 pages, 2548 KiB  
Article
Enhancing Multi-Step Reservoir Inflow Forecasting: A Time-Variant Encoder–Decoder Approach
by Ming Fan, Dan Lu and Sudershan Gangrade
Geosciences 2025, 15(8), 279; https://doi.org/10.3390/geosciences15080279 - 24 Jul 2025
Abstract
Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, [...] Read more.
Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, in this study, we propose a novel time-variant encoder–decoder (ED) model designed specifically to improve multi-step reservoir inflow forecasting, enabling accurate predictions of reservoir inflows up to seven days ahead. Unlike conventional ED-LSTM and recursive ED-LSTM models, which use fixed encoder parameters or recursively propagate predictions, our model incorporates an adaptive encoder structure that dynamically adjusts to evolving conditions at each forecast horizon. Additionally, we introduce the Expected Baseline Integrated Gradients (EB-IGs) method for variable importance analysis, enhancing interpretability of inflow by incorporating multiple baselines to capture a broader range of hydrometeorological conditions. The proposed methods are demonstrated at several diverse reservoirs across the United States. Our results show that they outperform traditional methods, particularly at longer lead times, while also offering insights into the key drivers of inflow forecasting. These advancements contribute to enhanced reservoir management through improved forecasting accuracy and practical decision-making insights under complex hydroclimatic conditions. Full article
(This article belongs to the Special Issue AI and Machine Learning in Hydrogeology)
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