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Evaluating deep learning methods for virtual sensors in water infrastructure digital twins

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dc.contributor.authorMaruvada, V.
dc.contributor.authorKaur, K.
dc.contributor.authorSelway, M.
dc.contributor.authorStumptner, M.
dc.contributor.conference17th International Conference on Computer and Automation Engineering-ICCAE-Annual (20 Mar 2025 - 22 Mar 2025 : Perth, Australia)
dc.date.issued2025
dc.description.abstractWater scarcity is an increasing global issue, with the demand for water potentially exceeding supply, and organizations failing to balance current and future needs. Continuous monitoring and accurate prediction of water availability, proactive leakage detection and optimising water consumption are crucial, necessitating advanced, cost-effective, and highly accurate methods for effective water management. The integration of the Internet of Things (IoT) with Artificial Intelligence (AI) in Industry 4.0 has significantly enhanced monitoring, automated industrial processes and enabled forecasting. High-quality data is essential to deliver precise insights and optimal performance. Virtual sensors, which digitally replicate failed IoT sensors, offer a smart solution for data generation, that impacts water infrastructure monitoring. This study employs deep learning models Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), and Variational Autoencoders (VAE), to generate synthetic data for virtual sensors. The findings indicate that LSTM provide better outputs, paving the way for further development and implementation of virtual sensors in Water Infrastructure Digital Twins.
dc.identifier.citation2025 17th International Conference On Computer And Automation Engineering, Iccae, 2025, pp.236-240
dc.identifier.doi10.1109/ICCAE64891.2025.10980577
dc.identifier.isbn9798331533823
dc.identifier.issn2154-4352
dc.identifier.urihttps://hdl.handle.net/11541.2/44031
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeUS
dc.relation.ispartofseriesInternational Conference on Computer and Automation Engineering
dc.rightsCopyright 2025 IEEE Access Condition Notes: Accepted manuscript is available open access
dc.source.urihttps://doi.org/10.1109/ICCAE64891.2025.10980577
dc.subjectwater industry
dc.subjectdigital twins
dc.subjectinternet of things
dc.subjectdeep learning
dc.subjectgenerative adversarial networks
dc.subjectvariational autoencoders
dc.subjectlong short-term memory
dc.subjectvirtual sensors
dc.subjectsynthetic data
dc.titleEvaluating deep learning methods for virtual sensors in water infrastructure digital twins
dc.typeConference paper
pubs.publication-statusPublished
ror.fileinfo12306786730001831 13306756740001831 Open Access Postprint
ror.mmsid9917056367301831

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