Big Data and Machine Learning for Hydrology Research: Methods, Applications and Future Directions
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: 27 August 2026 | Viewed by 2839
Special Issue Editors
Interests: water resources; hydrological modeling; sediment transport; spatial optimization; cellular automata; water distribution systems; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: hydrology; geoinformatics; machine Learning; supervised learning; clustering analysis; GIS; rainfall erosivity; climate change; soil erosion modelling; sediment transport
Special Issue Information
Dear Colleagues,
The science of hydrology is being profoundly transformed by decisive developments in machine learning, driven by the deluge of Big Data and the emergence of powerful tools ranging from random forests to advanced deep learning architectures such as LSTMs and transformers. These technologies offer a host of capabilities, uncovering complex, nonlinear patterns in the water cycle and revolutionizing hydrologic modeling by shifting from traditional physics-based to data-driven approaches. More recently, these two paradigms have increasingly been integrated in complementary ways. Furthermore, the flexibility of advanced ML is helping to blur the boundaries between hydrosystem modeling and management, enabling the development of unified software frameworks for both tasks.
The aim of this Special Issue, “Big Data and Machine Learning for Hydrology Research: Methods, Applications and Future Directions”, is to present the state-of-the-art in this rapidly evolving field. We invite authors to submit high-quality research articles, reviews, and concept papers that demonstrate the innovative application of Big Data and ML in hydrology. We are particularly interested in work that goes beyond prediction to address key challenges related to interpretability, physical consistency, and real-world application.
Topics of interest include, but are not limited to the following:
- Novel deep learning architectures (e.g., CNN, LSTM, Transformers) for hydrological forecasting.
- Large-sample hydrology studies leveraging benchmark datasets (e.g., CAMELS).
- Fusion of multi-source data (remote sensing, IoT, citizen science) in ML models.
- Explainable AI (XAI) for interpreting "black box" hydrological models.
- Physics-Informed Machine Learning (PIML) and hybrid modeling approaches.
- Development and application of Digital Twins for water systems.
- ML applications for flood, drought, water quality, and groundwater management.
- Uncertainty quantification and management through ML methods.
We welcome contributions from researchers, practitioners, and interdisciplinary teams working at the intersection of hydrology, data science, and engineering. Your work will help showcase the breadth and potential of machine learning in advancing hydrological science and practice.
Prof. Dr. Epaminondas Sidiropoulos
Guest Editor
Dr. Vantas Konstantinos
Guest Editor Assistant
Manuscript Submission Information
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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. Water is an international peer-reviewed open access semimonthly 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 2600 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.
Keywords
- machine learning
- deep learning architectures
- hydrological forecasting
- large-sample hydrology studies
- multi-source data fusion
- big data
- explainable AI
- physics-informed machine learning
- digital twins in water systems
- uncertainty quantification
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