Model-based methods for leakage localization in water distribution systems have recently been gaining more attention. These methods identify the leakage position by comparing the measured network data with the corresponding values simulated by a hydraulic model. In this study two model-based methods already proposed in literature, one based on the Sensitivity Matrix method and the other one on the Linear Approximation method, are analysed and compared to each other. The methods are applied to the same case study network, exploiting only data provided by pressure sensors. Various analyses are undertaken in order to investigate the main critical issues tied to the two methods, i.e. a) the use of different amounts of data averaged over different time windows, b) the impact of the model’s accuracy in terms of water demands and pipe roughness, and c) the effect of the number of pressure measuring points. The results show that higher efficiency is obtained by considering the hourly averaged data all together. Moreover, the Linear Approximation method is on average 3 times more accurate than the Sensitivity Matrix when a perfect hydraulic model is used, even with a reduced number of pressure sensors. However, when a hydraulic model and/or measured data affected by errors are considered, the Sensitivity Matrix is more accurate, with an average error almost 10% lower than the Linear Approximation.

A comparison of model-based methods for leakage localization in water distribution systems

Irene Marzola
Primo
;
Stefano Alvisi
Secondo
;
Marco Franchini
Ultimo
2022

Abstract

Model-based methods for leakage localization in water distribution systems have recently been gaining more attention. These methods identify the leakage position by comparing the measured network data with the corresponding values simulated by a hydraulic model. In this study two model-based methods already proposed in literature, one based on the Sensitivity Matrix method and the other one on the Linear Approximation method, are analysed and compared to each other. The methods are applied to the same case study network, exploiting only data provided by pressure sensors. Various analyses are undertaken in order to investigate the main critical issues tied to the two methods, i.e. a) the use of different amounts of data averaged over different time windows, b) the impact of the model’s accuracy in terms of water demands and pipe roughness, and c) the effect of the number of pressure measuring points. The results show that higher efficiency is obtained by considering the hourly averaged data all together. Moreover, the Linear Approximation method is on average 3 times more accurate than the Sensitivity Matrix when a perfect hydraulic model is used, even with a reduced number of pressure sensors. However, when a hydraulic model and/or measured data affected by errors are considered, the Sensitivity Matrix is more accurate, with an average error almost 10% lower than the Linear Approximation.
2022
Marzola, Irene; Alvisi, Stefano; Franchini, Marco
File in questo prodotto:
File Dimensione Formato  
2022_WaterResMan_Marzola.pdf

accesso aperto

Descrizione: Full text editoriale
Tipologia: Full text (versione editoriale)
Licenza: Creative commons
Dimensione 1.28 MB
Formato Adobe PDF
1.28 MB Adobe PDF Visualizza/Apri

I documenti in SFERA 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/11392/2499137
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 2
social impact