Innovative Modeling of Water Systems: Integrating AI, Digital Twins, and Observations
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: 30 September 2026 | Viewed by 158
Special Issue Editor
Interests: arctic geohazards; iceberg–structure–seabed interaction; computational fluid dynamics; machine learning; AI application
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The innovative modeling of water systems is undergoing a rapid transformation driven by advances in artificial intelligence (AI), digital twin technologies, and the expanding availability of high-resolution observations. Traditional process-based models, while foundational, often struggle to capture complex nonlinear dynamics, scale interactions, and real-time variability inherent in natural and engineered water systems. This summary presents an integrated framework that combines physics-based understanding with data-driven intelligence to enhance prediction, monitoring, and decision-making across hydrological, hydraulic, and water resource applications.
AI methods, including machine learning and deep learning, enable the extraction of patterns from heterogeneous data sources such as remote sensing, in situ sensors, and socio-environmental datasets. When embedded within digital twins, dynamic virtual replicas of physical water systems, these methods support continuous model updating, scenario testing, and early warning capabilities. Observational data play a critical role by constraining models, reducing uncertainty, and enabling real-time assimilation for adaptive system management.
The integration of AI, digital twins, and observations facilitates the improved forecasting of floods and droughts, optimized infrastructure operation, enhanced water quality assessment, and more resilient planning under climate and land-use change. However, challenges related to data quality, model interpretability, computational demands, and the need to balance physical realism with algorithmic flexibility remain. Addressing these issues requires interdisciplinary collaboration, transparent modeling practices, and robust validation strategies.
Overall, this integrated modeling paradigm represents a significant step toward intelligent, adaptive, and scalable water system management, offering actionable insights for researchers, practitioners, and policy makers facing increasing water-related risks and uncertainties. Such approaches support sustainable, equitable, and evidence-based water governance across the world.
Dr. Hamed Azimi
Guest Editor
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Keywords
- iceberg modeling
- artificial intelligence
- digital twins
- water system modeling
- data assimilation
- hydrological observations
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