Pressure sensor placement in water distribution networks for leak detection using a hybrid information-entropy approach

Mohammad Sadegh Khorshidi, Mohammad Reza Nikoo*, Narges Taravatrooy, Mojtaba Sadegh, Malik Al-Wardy, Ghazi Ali Al-Rawas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

This study proposes an optimization framework based on a hybrid information-entropy approach to identify leakage events in water distribution networks (WDN). Optimization-based methods are widely employed in the literature for such purposes; however, they are constrained by time-consuming procedures. Hence, researchers eliminate parts of the decision space to curtail the computational burden. Here, we propose an information theory-based approach, using Value of Information (VOI) and Transinformation Entropy (TE) methods, in conjunction with an optimization model to explore the entire decision space. VOI allows for the entire feasible space search through intelligent sampling, which in turn ensures robust solutions. TE minimizes redundant information and helps maximize the spatial distribution of sensors. The herein proposed model is developed within a multi-objective optimization framework that renders a set of Pareto-optimal solutions. ELimination and Choice Expressing the REality (ELECTRE) multi-criteria decision-making model is then used to select the best compromise solution given several weighting scenarios. The results of this study show that the information-entropy based scheme can improve the precision of leak detection by enhancing the decision space, and can reduce the computational burden.

Original languageEnglish
Pages (from-to)56-71
Number of pages16
JournalInformation Sciences
Volume516
DOIs
Publication statusPublished - Apr 2020

Keywords

  • ELECTRE model
  • Leak detection
  • Pressure sensor placement
  • Transinformation entropy
  • Value of information
  • Water distribution network

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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