ID:
publications-1867
Type:
Peer reviewed articles
Year:
2024
Authors:
Prasanna Mohan Doss, Marius Møller Rokstad, Franz Tscheikner-Gratl
Title:
The performance of encoder–decoder neural networks for leak detection in water distribution networks
Venue/Journal:
Water Supply
DOI:
10.2166/ws.2024.174
Research type:
Uncategorized
Water System:
River Basins
Technical Focus:
Abstract:
ABSTRACT This work outlines the performance of three variants of deep neural networks for leak detection in water distribution networks, namely – autoencoders (AEs), variational autoencoders (VAEs), and long short-term memory autoencoders (LSTM-AEs). The multivariate pressure signals reconstructed from these models are analysed for leakage identification. The leak onset time is estimated using a fast approximation sliding window technique, which computes statistical discrepancies in prediction errors. The performance of all three variants is validated using the widely studied L-Town benchmark network. Furthermore, their feasibility for real-world application is studied by applying them to a real-world case study representing the data availability and network design often found in smaller- and medium-sized utilities in Norway. The results for the benchmark network showed that AE and LSTM-AE showed comparable detection performance for abrupt leaks with VAE performing the least. For incipient leaks, the LSTM-AE showed better detection performance with few false-positives. For the real-world dataset, the performance was significantly lower due to the quantity and quality of data available, and the contradiction of inherent requirements of data-driven models. In addition, the analysis revealed that the positioning of pressure sensors in the network is critical for the leak detection performance of these models.
Link with Projects:
869171
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