Data-driven Leak Localization in Water Distribution Networks via Dictionary Learning and Graph-based Interpolation
This addresses leak detection for water utilities, but it is incremental as it builds on existing methods.
The paper tackles leak localization in water distribution networks by combining graph-based interpolation with dictionary learning, achieving superior performance compared to using either method alone, as validated on the L-TOWN benchmark.
In this paper, we propose a data-driven leak localization method for water distribution networks (WDNs) which combines two complementary approaches: graph-based interpolation and dictionary classification. The former estimates the complete WDN hydraulic state (i.e., hydraulic heads) from real measurements at certain nodes and the network graph. Then, these actual measurements, together with a subset of valuable estimated states, are used to feed and train the dictionary learning scheme. Thus, the meshing of these two methods is explored, showing that its performance is superior to either approach alone, even deriving different mechanisms to increase its resilience to classical problems (e.g., dimensionality, interpolation errors, etc.). The approach is validated using the L-TOWN benchmark proposed at BattLeDIM2020.