An approach based on hydraulic simulation and machine learning is presented, aimed at improving leakage management via analytical leak localization and reducing time and costs for investigation and rehabilitation of the Water Distribution Network. Hydraulic simulation is used to run different leakage scenarios by introducing a leak on each pipe, in turn, and varying its severity. The approach has been validated on two WDNs: a Pressure Management Zone in Milan (Italy) and a District Metered Area in Timisoara (Romania), the two pilots of the EU-FP7-ICT project ICeWater, obtaining a high reliability (>90%) in localizing a wide set of simulated leaks

Candelieri, A., Soldi, D., Conti, D., Archetti, F. (2014). Analytical Leakages Localization in Water Distribution Networks through Spectral Clustering and Support Vector MACHINES. The Icewater Approach. PROCEDIA ENGINEERING, 89, 1080-1088 [10.1016/j.proeng.2014.11.228].

Analytical Leakages Localization in Water Distribution Networks through Spectral Clustering and Support Vector MACHINES. The Icewater Approach

Candelieri, A;Archetti, FA
2014

Abstract

An approach based on hydraulic simulation and machine learning is presented, aimed at improving leakage management via analytical leak localization and reducing time and costs for investigation and rehabilitation of the Water Distribution Network. Hydraulic simulation is used to run different leakage scenarios by introducing a leak on each pipe, in turn, and varying its severity. The approach has been validated on two WDNs: a Pressure Management Zone in Milan (Italy) and a District Metered Area in Timisoara (Romania), the two pilots of the EU-FP7-ICT project ICeWater, obtaining a high reliability (>90%) in localizing a wide set of simulated leaks
Articolo in rivista - Articolo scientifico
leakage management; leak localization; spectral clustering; support vector machine; simulation
English
2014
89
1080
1088
open
Candelieri, A., Soldi, D., Conti, D., Archetti, F. (2014). Analytical Leakages Localization in Water Distribution Networks through Spectral Clustering and Support Vector MACHINES. The Icewater Approach. PROCEDIA ENGINEERING, 89, 1080-1088 [10.1016/j.proeng.2014.11.228].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/59603
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