Criteria-based critical review of artificial intelligence applications in water-leak management

Sherif Abdelmageed, Salman Tariq, Vincent Boadu, Tarek Zayed

Research output: Journal article publicationReview articleAcademic researchpeer-review

Abstract

Leakages in water distribution networks (WDNs) cause economic losses and environmental hazards. It is, therefore, unsurprising that water-leak management has been a focus of research over the last couple of decades, but leaks in WDNs still occur frequently. Thus, this domain is experiencing a transformation from traditional signal processing and statistical-based models to artificial intelligence (AI) based models for recognizing complex leak patterns, handling large datasets, and establishing accurate leak-management models, especially in leak detection and localization. However, a comprehensive review of the application of AI in water-leak management is largely missing from the literature. To bridge this gap, this review presents a criteria-based critical review to systematically investigate the existing literature on the application of AI in four sub-domains of leak management including leak detection, localization, prediction, and sizing. The first criterion (research attributes) established the (1) research trends, (2) links between influential countries and sources, and (3) popular keywords using scientometric analysis. The systematic analysis of the second criterion (research technicality) and the third criterion (research focus) revealed the (1) AI-techniques adopted, (2) equipment used for collecting data, (3) data features used in the models, (4) objectives of different models adopted, (5) type of experiments conducted to collect the data, and (6) types of pipes for which models were developed. The study highlighted research gaps, future research directions, and proposed a leak management framework for upcoming AI studies in this domain. This review is intended to serve early researchers by enhancing their understanding of existing research in AI-based leak management as well as seasoned researchers by providing a platform for future research.

Original languageEnglish
Pages (from-to)280-297
Number of pages18
JournalEnvironmental Reviews
Volume30
Issue number2
DOIs
Publication statusPublished - Mar 2022

Keywords

  • artificial intelligence
  • criteria-based review
  • water-leak management

ASJC Scopus subject areas

  • Environmental Science(all)

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