Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17224357

Authors: Vinokić, Luka, Milašinović, Miloš, Vasilic, Zeljko, Ivetić, Damjan, Stojkovic, Milan, Prodanovic, Veljko

Institutions: Istraživačko-razvojni institut za veštačku inteligenciju Srbije, University of Belgrade

Language: English

Topic: Urban Stormwater Management Solutions

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Clean water and sanitation

Open Access status: green

Funder: European Commission