Authors:
Vineeth Maruvada
;
Karamjit Kaur
;
Matt Selway
and
Markus Stumptner
Affiliation:
Industrial AI, University of South Australia, Adelaide, Australia
Keyword(s):
Virtual Sensors, Digital Twins, Water Infrastructure, Artificial Intelligence, Machine Learning, Deep Learning, Long Short Term Memory, XGBoost, Generative Adversarial Networks, Industry 4.0, Water Utilities.
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, Virtu
al Sensors can transform water utilities by improving environmental sustainability, operational intelligence, adaptability, and resilience within Digital Twins.
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