Digital Twins for Water and Environmental Systems: Intelligent Monitoring, Predictive Analytics, and Adaptive Decision-Making
The rapid convergence of environmental sensing, Internet of Things (IoT), artificial intelligence (AI), remote sensing, and advanced computational modeling is reshaping the future of water and environmental management. Among emerging paradigms, Digital Twins (DTs) have attracted growing attention as dynamic, data-driven frameworks capable of integrating real-time monitoring, predictive analytics, and adaptive decision-making within unified virtual representations of physical environmental systems.
As environmental systems become increasingly complex, interconnected, and vulnerable to climatic and anthropogenic pressures, Digital Twins are emerging as a critical next-generation approach for transitioning from reactive management toward predictive and adaptive environmental governance. Beyond conventional modeling approaches, Digital Twins are increasingly recognized for their potential to advance environmental intelligence, enabling more resilient, efficient, and proactive management of water resources, environmental infrastructure, ecosystem services, and climate-related risks.
Recent advances in AI, cloud computing, geospatial analytics, and cyber-physical systems are accelerating the application of Digital Twins across urban water systems, watershed management, pollution monitoring, environmental restoration, sustainability planning, and risk assessment. Nevertheless, major scientific and practical challenges remain regarding interoperability, uncertainty quantification, explainable AI, multi-source data integration, scalability, and governance-oriented implementation.
This Special Issue aims to provide a forward-looking interdisciplinary platform for cutting-edge research and real-world applications that bridge monitoring, modeling, prediction, and intelligent environmental decision-making through next-generation Digital Twin technologies.
Themes and Topics of Interest
The Special Issue welcomes original research articles, critical reviews and perspectives related to (but not limited to) the following topics:
- Digital Twin Architectures and Environmental Intelligence
- Digital Twin frameworks for water and environmental systems
- Cyber-physical environmental systems
- Integrated environmental modeling platforms
- Hybrid physics-informed and data-driven Digital Twins
- Smart Sensing, Data Integration, and Real-Time Monitoring
- Real-time environmental sensing and IoT integration
- Sensor networks and intelligent monitoring systems
- Remote sensing and geospatial data assimilation
- Big data management for environmental systems
- AI-Enabled Prediction and Adaptive Decision Support
- Machine learning and deep learning in Digital Twins
- Predictive analytics and intelligent forecasting
- Explainable AI for environmental decision support
- Edge computing and cloud-based environmental intelligence
- Applications in Water, Infrastructure, and Ecosystem Management
- Urban water systems and smart utilities
- Watershed and river basin Digital Twins
- Groundwater and hydrological system modeling
- Wastewater treatment and infrastructure optimization
- Coastal and marine environmental monitoring
- Ecosystem dynamics and environmental restoration
- Resilience, Risk, and Sustainability under Global Change
- Climate adaptation and resilience planning
- Early warning systems and hazard prediction
- Environmental risk assessment and scenario analysis
- Sustainable infrastructure management
- Governance, Ethics, and Future Perspectives
- Digital governance for environmental systems
- Policy implications and regulatory considerations
- Standardization, interoperability, and ethics
Future directions for environmental Digital Twins