Digital Twins of Urban Drainage Systems: innovative data assimilation algorithm for continuous state update

Urban drainage systems (UDSs) are facing increasing challenges due to aging infrastructure and external factors, requiring innovative decision support solutions. Digital Twins (DT) of the real-world systems offer a promising decision-support tool for UDS management. These digital replicas, updated in real-time through sensor data integration, can assist with energy efficiency, resource allocation, and scenario analysis for contingency planning. DTs require up-to-date simulation model, and integration of the sensor data into the model is perceived as a critical component. This research introduces an innovative data assimilation method, utilizing Proportional-Integrative-Derivative (PID) controllers to update UDS model states based on sensor data. Proposed approach, tested with PySWMM on a synthetic dataset, demonstrates the potential for improving UDS performance and reducing uncertainty through continuous updates.