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A hybrid leak detection framework using variational autoencoder surrogates

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Abstract

Traditionally, model-based approaches are widely used for leak detection in Water Distribution Networks (WDNs). However, these methods are computationally expensive for larger networks and are highly dependent on the model accuracy. In this work, we propose a hybrid modelling framework for leak detection, wherein, a surrogate model using a variational autoencoder (VAE) neural network is trained with a calibrated hydraulic model. The statistical measures on the performance of VAEs are then used for leak detection. The efficacy of the proposed framework is tested on a theoretical WDN and a real-world WDN using data from steady-state hydraulic simulations and sensor measurements.
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Dates and versions

hal-03749707 , version 1 (11-08-2022)

Licence

Attribution - CC BY 4.0

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  • HAL Id : hal-03749707 , version 1

Cite

Prasanna Mohan Doss, David B Steffelbauer, Olivier Piller, Marius Møller Rokstad, Franz Tscheikner-Gratl. A hybrid leak detection framework using variational autoencoder surrogates. WaterLoss2022, IWA Water Loss Specialist Group, Jun 2022, Prague, Czech Republic. ⟨hal-03749707⟩
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