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Finally, a WSMC project in China is taken as a case study, and its interruption risks are analysed. Third, the risk tolerance threshold is given to estimate the interruption result, and its different consequences are discussed. Second, the DES is divided into two parts: the duration caused by COVID-19 and that under other types of emergencies.
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First, the concept of the interruption risk index (IRI) is defined as a function of the duration of enterprise shutdown (DES). An interruption risk assessment model of WSMC projects is established through a quantitative evaluation of the impact of emergencies on water users based on input-output theory. 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.Īs a novel market-based water-saving mechanism, the Water Saving Management Contract (WSMC) project faces interruption risk caused by emergencies like the coronavirus disease-2019 (COVID-19) pandemic. The study highlighted research gaps, future research directions, and proposed a leak management framework for upcoming AI studies in this domain. 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 first criterion (research attributes) established the (1) research trends, (2) links between influential countries and sources, and (3) popular keywords using scientometric analysis.
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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. However, a comprehensive review of the application of AI in water-leak management is largely missing from the literature. 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. 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. Leakages in water distribution networks (WDNs) cause economic losses and environmental hazards. It illustrates that the proposed algorithm, integrated with a GIS-based spatial flow data analysis, efficiently supports early detection, likelihood severity assessment, and geolocation of leak sources. The purpose of this research was to develop, test, validate, and illustrate the application of the machine-learning–based risk assessment method for early detection of high likelihood leaks, their geolocation, and the detection accuracy assessment in the water distribution system of the SUNRISE demonstration site at the University of Lille, France. The detection of the occurrence of a potential leak in a DMA is established through a systematic continuous comparison of the real-time distributed volume and the consumption for this DMA and/or, in the absence of AMR, the comparison of the monitored distributed volume and a reference curve based upon past monitoring records of the distributed volume under similar operational conditions.
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Current leak detection practice in a water distribution system consists of monitoring the distributed volume in a district metering area (DMA) and the consumption measured with automated meter reading (AMR) at the building connections.