New trends in Hydrology and Integrated Water-Resource Management utilizing AI and ML potentials
A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Water Resources and Risk Management".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 3
Special Issue Editors
Interests: water supply networks; water resources; hydraulics; fluid mechanics
Special Issues, Collections and Topics in MDPI journals
Interests: hydraulics of closed and open conduits; flows of closed and open conduits; experimental measurements (hot-film anemometry; particle image velocimetry); computational hydraulics; flows in porous media; gravity currents in lock-exchange experiments; buoyant jets in closed and open conduits
Special Issues, Collections and Topics in MDPI journals
Interests: innovative management techniques for water resources and for water systems; seismic reliability of hydraulic structures; risk assessment for transportation and storage facilities in water systems; hydraulic protection of territories; experimental analysis on hydraulic prototypes; stormwater tanks and overflow chambers for the mitigation of pollution caused by first flushes in sewers; probabilistic approaches for the evaluation of the effects of climate changes on river flows and land desertification
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hydrology and Integrated Water-Resource Management (IWRM) are experiencing rapid methodological evolution as artificial intelligence (AI) and machine learning (ML) become increasingly embedded in analytical workflows, forecasting pipelines and decision-support processes. These technologies are reshaping how complex hydrological processes are interpreted, enabling faster and more rigorous analysis of large, multi-source datasets and supporting more agile and forward-looking resource management. In an era marked by fluctuating water availability, intensifying operational pressures and increasing demands for resilience, AI and ML provide powerful avenues for elevating modelling accuracy, strengthening predictive insight and enhancing the alignment between scientific assessment and practical decision-making.
This Special Issue highlights the transformative potential of advanced data-driven approaches to improve hydrological understanding and optimize integrated water-resource management, showcasing innovative methodologies that can drive more efficient, informed and sustainable water system governance.
The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights about new trends in hydrology and integrated water-resource management utilizing AI and ML potentials.
The aim of this Special Issue is to gather original research articles and review papers that examine how AI and ML applications advance hydrological analysis and strengthen IWRM approaches. The scope aligns with the mission of Hydrology, which promotes scientifically grounded contributions that improve process understanding, forecasting and water-related decision-making.
Additionally, selected papers from the 6th International Conference on Efficient Water Systems (EwaS6), entitled “Safeguarding Water and Health in a Financially, Socially, and Environmentally Fragile Era,” will be published in this Special Issue, further enriching its scientific and practical relevance.
By uniting expertise from hydrology, water engineering, data science and systems management, this collection aims to highlight innovative methodologies, applied case studies and emerging pathways enabled by AI- and ML-based tools.
This Special Issue will welcome manuscripts that link the following themes:
- AI- and ML-enabled hydrological modelling and prediction
- Data-driven analysis for surface water and groundwater systems
- AI-supported flood, drought and water-availability forecasting
- Integration of digital tools in IWRM planning and optimization
- Remote sensing and geospatial data analytics supported by ML methods
- Decision-support systems combining physical models with AI components
- Real-time monitoring, anomaly detection and sensor network analytics
- Hybrid approaches linking process-based modelling with data-driven methods
Both original research articles and review papers are invited. Submissions demonstrating methodological innovation, performance assessment, or practical application of AI and ML in hydrology and IWRM are strongly encouraged.
We look forward to receiving your original research articles and reviews.
Prof. Dr. Vasilis Kanakoudis
Dr. Evangelos Keramaris
Prof. Dr. Francesco De Paola
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. Hydrology 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.
Keywords
- water systems
- artificial intelligence
- machine learning
- digital tools
- water quality
- water resources
- floods
- drought
- water cycle
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