Digital Twin Technology for River Basin Management: A Framework for Proactive Flood Mitigation and Water Resource Optimization

Document Type

Conference Article

Publication Title

Iet Conference Proceedings

Abstract

The increasing frequency of extreme weather events due to climate change is leading to more incidents of river flooding and water clogging. Digital twin technology provides a promising solution for modelling and mitigating these issues by creating a virtual replica of the physical river system. This paper explores the development of a digital twin model specifically for monitoring and predicting water clogging and flow patterns in rivers. From acquiring real-time sensor data on river levels, precipitation, and environmental conditions to processing this multidimensional data, the digital twin model integrates physics-based hydraulic simulations with data-driven machine learning models. The 3D virtual environment precisely mirrors the river's geometry, terrain, infrastructure like bridges and dams, and dynamic variables like water velocity and depth. The digital twin continuously maps the state of the physical river by assimilating live sensor feeds. Machine learning models calibrated on historical data help forecast river flow rates, water depths, potential clogging areas, and flood risks. Physics simulations incorporate these predictions along with the river's characteristics to model the flow dynamics accurately. This synergy enables proactive monitoring, early warning systems, testing mitigation strategies, and optimizing reservoir operations. The proposed digital twin model for river clogging and flow lays the foundation for digital river basin engineering - an integrated pipeline spanning data acquisition, modelling, simulation, decision support, and control actions. The results can guide water resource management, urban planning near rivers, and disaster preparedness, ultimately enhancing resilience against flooding events.

First Page

660

Last Page

665

DOI

10.1049/icp.2025.0868

Publication Date

1-1-2024

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