The identifiability analysis is investigated as sampling design method aimed to the leakagedetection in looped water distribution networks. The preliminary ranking of the candidate nodes forthe pressure sensors positioning is performed by running several hydraulic simulations andcalculating sensitivity functions. The reduced subset of nodes and their sensitivities are then used toperform the identifiability analysis by calculating the collinearity index which provides themaximum number of sensors and their location into the network. The index selects the nodesaccording to their sensitivities to several leakages scenarios, simulated in EPANET by changingthe emitter coefficient of the leakages function both with a One-At-Time and Monte Carloapproach. The collinearity index also identifies the subset of the pressure monitoring nodes withthe lowest correlation (redundancy) between the measurements. The method is applied to thebenchmark network Apulian.

title = "CCWI2017: F79 'Identifiability analysis for pressure sensors positioning'",

abstract = "The identifiability analysis is investigated as sampling design method aimed to the leakagedetection in looped water distribution networks. The preliminary ranking of the candidate nodes forthe pressure sensors positioning is performed by running several hydraulic simulations andcalculating sensitivity functions. The reduced subset of nodes and their sensitivities are then used toperform the identifiability analysis by calculating the collinearity index which provides themaximum number of sensors and their location into the network. The index selects the nodesaccording to their sensitivities to several leakages scenarios, simulated in EPANET by changingthe emitter coefficient of the leakages function both with a One-At-Time and Monte Carloapproach. The collinearity index also identifies the subset of the pressure monitoring nodes withthe lowest correlation (redundancy) between the measurements. The method is applied to thebenchmark network Apulian.",

author = "Valeria Puleo and {La Loggia}, Goffredo and Gabriele Freni",

year = "2017",

language = "English",

}

TY - CONF

T1 - CCWI2017: F79 'Identifiability analysis for pressure sensors positioning'

AU - Puleo, Valeria

AU - La Loggia, Goffredo

AU - Freni, Gabriele

PY - 2017

Y1 - 2017

N2 - The identifiability analysis is investigated as sampling design method aimed to the leakagedetection in looped water distribution networks. The preliminary ranking of the candidate nodes forthe pressure sensors positioning is performed by running several hydraulic simulations andcalculating sensitivity functions. The reduced subset of nodes and their sensitivities are then used toperform the identifiability analysis by calculating the collinearity index which provides themaximum number of sensors and their location into the network. The index selects the nodesaccording to their sensitivities to several leakages scenarios, simulated in EPANET by changingthe emitter coefficient of the leakages function both with a One-At-Time and Monte Carloapproach. The collinearity index also identifies the subset of the pressure monitoring nodes withthe lowest correlation (redundancy) between the measurements. The method is applied to thebenchmark network Apulian.

AB - The identifiability analysis is investigated as sampling design method aimed to the leakagedetection in looped water distribution networks. The preliminary ranking of the candidate nodes forthe pressure sensors positioning is performed by running several hydraulic simulations andcalculating sensitivity functions. The reduced subset of nodes and their sensitivities are then used toperform the identifiability analysis by calculating the collinearity index which provides themaximum number of sensors and their location into the network. The index selects the nodesaccording to their sensitivities to several leakages scenarios, simulated in EPANET by changingthe emitter coefficient of the leakages function both with a One-At-Time and Monte Carloapproach. The collinearity index also identifies the subset of the pressure monitoring nodes withthe lowest correlation (redundancy) between the measurements. The method is applied to thebenchmark network Apulian.