Our paper "Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks " has been accepted to LWDA KDML 2023

Abstract:

In this paper, we introduce the Liquor-HGNN model, a novel approach for detecting and localizing
leaks in drinking water distribution networks (DWDNs) through the utilization of heterogeneous graph
learning. By leveraging a preprocessing model, our approach mounts the challenges posed by data
sparsity and sensor heterogeneity limitations. Liquor-HGNN outperforms all other approaches on the
same dataset in terms of Economic score. Here, the Economic Score function iterates over the detected
leakages, finds the closest pipes to each detected leakage, and calculates the score contribution for each
true detection based on the detected distance as well as on the starting time of the leakages. To the best
of our knowledge, Liquor-HGNN represents the first-ever application of a heterogeneous Graph Neural
Network (GNN) specifically tailored for leak detection in DWDNs.