Digital Twins of Urban Drainage Systems: ML-assisted algorithm for processing sensor data

Deploying sensors network and collecting and using sensor data is a backbone of Digital Twins (DTs) for engineering systems, such as Urban Drainage Systems (UDS). Such data often exhibit missing values and anomalous readings due to many factors (e.g. sensors malfunction, hardware limitations, weather and site conditions). System analytics in DTs rely on these data and requires postprocessing algorithms capable to detect and reduce problems in collected data. This research aims to develop an advanced ML-powered algorithm for automated data anomaly detection (data validation) and estimation of missing data. This algorithm utilizes an ensemble of ML models to address data quality issues. The algorithm is tested on a synthetic dataset for a part of Belgrade stormwater system.