Back to Search Start Over

Data-driven Leak Localization in Water Distribution Networks via Dictionary Learning and Graph-based Interpolation

Authors :
Irofti, Paul
Romero-Ben, Luis
Stoican, Florin
Puig, Vicenç
Publication Year :
2021

Abstract

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2110.06372
Document Type :
Working Paper