Towards Machine Learning Driven Virtual Sensors for Smart Water Infrastructure

Vineeth Maruvada, Karamjit Kaur, Matt Selway, Markus Stumptner

2025

Abstract

Water utilities around the world are under increasing pressure from climate change, urban expansion, and aging infrastructure. To address these challenges, smarter and more sustainable water management solutions are essential. This study explores the use of Machine Learning (ML) to develop Virtual Sensors for smart water infrastructure. Virtual Sensors can complement or replace physical sensors while improving environmental sustainability and enabling reliable and cost-effective Digital Twins (DTs). Our experimental results show that several ML models outperform traditional methods such as Auto-Regressive Integrated Moving Average (ARIMA) in terms of forecast accuracy and timeliness. Among these, Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) offer the best balance between accuracy and robustness. This research provides preliminary evidence that ML models can enable Virtual Sensors capable of delivering short-term forecasts. When successfully implemented, Virtual Sensors can transform water utilities by improving environmental sustainability, operational intelligence, adaptability, and resilience within Digital Twins.

Paper Citation

in Harvard Style

Maruvada V., Kaur K., Selway M. and Stumptner M. (2025). Towards Machine Learning Driven Virtual Sensors for Smart Water Infrastructure. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 453-460. DOI: 10.5220/0013721200003982

in Bibtex Style

@conference{icinco25,
author={Vineeth Maruvada and Karamjit Kaur and Matt Selway and Markus Stumptner},
title={Towards Machine Learning Driven Virtual Sensors for Smart Water Infrastructure},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2025},
pages={453-460},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013721200003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Towards Machine Learning Driven Virtual Sensors for Smart Water Infrastructure
SN - 978-989-758-770-2
AU - Maruvada V.
AU - Kaur K.
AU - Selway M.
AU - Stumptner M.
PY - 2025
SP - 453
EP - 460
DO - 10.5220/0013721200003982
PB - SciTePress