This paper extends previous research to analytically identify leaks within a water distribution network (WDN), by combining hydraulic simulation and network science based data analysis techniques. The WDNmodel is used to run several 'leakage scenarios', by varying leak location (pipe) and severity, and to build a datasetwith corresponding variations in pressure and flow, induced by the leak. All junctions and pipes are considered for potential pressure and flow sensors deployment; a clustering procedure on these locations identifies the most relevant nodes and pipes, and cost-effectiveness was considered. A graph is then generated from the dataset, having scenarios as nodes and edges weighted by the similarity between each pair of nodes (scenarios), in terms of pressure and flowvariation due to the leak. Spectral clustering groups together similar scenarios in the eigen-space spanned by the most relevant eigen-vectors of the Laplacian matrix of the graph. This method uses superior traditional techniques. Finally, support vectormachines classification learning is used to learn the relation between variations in pressure and flow at the deployed meters and the most probable set of pipes affected by the leak.

Candelieri, A., Soldi, D., Archetti, F. (2015). Cost-effective sensors placement and leak localization - The Neptun pilot of the ICeWater project. JOURNAL OF WATER SUPPLY: RESEARCH AND TECHNOLOGY. AQUA, 64(5), 567-582 [10.2166/aqua.2015.037].

Cost-effective sensors placement and leak localization - The Neptun pilot of the ICeWater project

CANDELIERI, ANTONIO
Primo
;
ARCHETTI, FRANCESCO ANTONIO
Ultimo
2015

Abstract

This paper extends previous research to analytically identify leaks within a water distribution network (WDN), by combining hydraulic simulation and network science based data analysis techniques. The WDNmodel is used to run several 'leakage scenarios', by varying leak location (pipe) and severity, and to build a datasetwith corresponding variations in pressure and flow, induced by the leak. All junctions and pipes are considered for potential pressure and flow sensors deployment; a clustering procedure on these locations identifies the most relevant nodes and pipes, and cost-effectiveness was considered. A graph is then generated from the dataset, having scenarios as nodes and edges weighted by the similarity between each pair of nodes (scenarios), in terms of pressure and flowvariation due to the leak. Spectral clustering groups together similar scenarios in the eigen-space spanned by the most relevant eigen-vectors of the Laplacian matrix of the graph. This method uses superior traditional techniques. Finally, support vectormachines classification learning is used to learn the relation between variations in pressure and flow at the deployed meters and the most probable set of pipes affected by the leak.
Articolo in rivista - Articolo scientifico
Water Distribution Networks; Leakage Management; Leakage Localization; Spectral Clustering; Support Vector Machines, Classification, Graph based approach.
English
2015
64
5
567
582
none
Candelieri, A., Soldi, D., Archetti, F. (2015). Cost-effective sensors placement and leak localization - The Neptun pilot of the ICeWater project. JOURNAL OF WATER SUPPLY: RESEARCH AND TECHNOLOGY. AQUA, 64(5), 567-582 [10.2166/aqua.2015.037].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/59504
Citazioni
  • Scopus 19
  • ???jsp.display-item.citation.isi??? 14
Social impact