Publications by authors named "Zhenheng Tang"
Machine learning has been increasingly used to solve management problems of water distribution networks (WDNs). A critical research gap, however, remains in the effective incorporation of WDN hydraulic characteristics in machine learning. Here we present a new water distribution network embedding (WDNE) method that transforms the hydraulic relationships of WDN topology into a vector form to be best suited for machine learning algorithms.
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- A new framework called Burst Location Identification Framework by Fully-linear DenseNet (BLIFF) uses strategically placed pressure meters and advanced deep learning techniques to accurately locate pipe bursts.
- Testing on both benchmark networks and real-life cases demonstrated BLIFF's effectiveness, successfully identifying most burst locations amid uncertainties, outperforming traditional methods.