Improving the leak detection efficiency in water distribution networks using noise loggers

Tijani, I. A.; Abdelmageed, Sherif; Fares, Ali; Fan, Kuan; Hu, Zhongyu; Zayed, Tarek

doi:10.1016/j.scitotenv.2022.153530

Cited by 32 publications

(12 citation statements)

References 45 publications

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“…For the condition of dense urban environments, challenges of ambient noise, lack of site details, difficulty in finding a location for deploying the sensors, false alarms and missed leaks pose major challenges in finding leaks. Therefore, an example experimental design for a real WDN in Hong Kong is presented as an example of collecting and analyzing leak data in dense urban environments (Tariq et al, 2022; Tijani et al, 2022; Tijani & Zayed, 2022). Both the experimental design and the process/protocol of conduction of experiments have been discussed in‐depth and implementation challenges are evaluated as useful for similar urban setting leak diagnosis.…”

Section: Methodsmentioning

confidence: 99%

“…Detection, localization, and pinpointing of water leaks are of interest to a wide variety of water practitioners. Leak detection refers to confirming the presence of a leak in a pipe network (Tijani et al, 2022), leak localization refers to estimating the position of the leak on the pipe network (Zaman et al, 2020), and leak pinpointing refers to identify the exact location of the leak on the pipeline (Chatzigeorgiou, Youcef‐Toumi, et al, 2011). For this purpose, vast empirical research has been conducted for studying the different aspects of leak detection and localization experimentation and analyses.…”

Section: Introductionmentioning

confidence: 99%

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State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

Bakhtawar

Zayed

2023

WIREs Water

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Effective water pipeline leak detection and localization can help water practitioners save water losses and reduce pipe rehabilitation budgets. Different sensing technologies can provide essential data aiding model development for predicting future leaks in the water distribution networks. Solutions for applying different sensing technologies to real water distribution networks are being developed for real‐time data collection, precise leak localization, reducing false alarms, and continuous pipeline monitoring. Experiments of different scales and specifications have been conducted to test the application and effectiveness of sensing technologies for leak diagnosis in water pipelines. Quality data collection is especially crucial for the successful implementation of these experiments. However, practitioners need help designing effective leak detection and localization experiments, wasting time, and resources. There needs to be a greater understanding of the design parameters for more accurate testing, such as sensor selection, sensor placement, ambient noise sources, and system operations. The current study, therefore, delineates design parameters for design leak diagnosis experiments for lab, testbed, and real networks. For application in real water networks, expert opinion is used to help identify the benefits and limitations and the implementation issues of available sensing technologies. An example experiment design using multi‐sensing technologies for smart leak diagnostics in Hong Kong is also presented. The study can guide water practitioners and researchers to design a multi‐sensing leak diagnostic experiments enabling a smart and sustainable utility management.This article is categorized under: Engineering Water > Methods Engineering Water > Sustainable Engineering of Water Science of Water > Water Extremes

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

Bakhtawar

Zayed

2023

WIREs Water

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“…For the case of no-leak, a peak will be shown in the low-frequency zone representing the system sounds. experiments as well (Cody & Narasimhan, 2020;Tijani et al, 2022). Therefore, a similar LP pre-processing has been adopted in this study for real water distribution networks.…”

Section: Signal Analysismentioning

confidence: 99%

“…Contrarily, the comparative simple tree-based models may not provide satisfactory accuracy results (Fan et al, 2022b). Although some studies have employed AI classifiers for leak detection and identification for other acoustic devices (Tijani et al, 2022)…”

Section: Introductionmentioning

confidence: 99%

AI-based smart water leak detection using hydrophones

Bakhtawar

Fares

Zayed

2022

Preprint

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Acoustic technologies are popular for the detection of leak detriments in water pipelines. However, problems of false alarms, missed leaks, limited site information, and the high cost of long-term monitoring remain prevalent. These issues demand a more sophisticated testing approach suitable for real-world applications. Hydrophone technology has a strong promise for precision leak detection. However, acoustic leak detection is mostly focused on detection using controlled testbed experiments. The practical application of hydrophones for leak detection has not been well reported in the literature. The current study presents a smart real-time leak detection system that uses real-time acoustic data collection. AI-based data-driven models were developed to identify leaks based on limited site information. Different classification models were trained using various feature combinations to identify the most significant model and feature set. ensemble-based classifiers of Adaboost, and Random Forest demonstrated the most promising performance for the leak detection application. Results reveal hydrophones to be more effective as compared to other acoustic devices like accelerometers and noise loggers in detecting leaks.

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“…Most of the research on static leak detection systems is focused on the adequate estimation of the signals transmitted from the devices to the central server to detect an actual leak Mohamed et al [2012], Tijani et al [2022]. A few works analyze the optimal placement of a given number of static devices on a finite number of potential placements based on the capability of each of the potential places to detect a leak Venkateswaran et al [2018], or in the use of historic data to place the devices at the more convenient places Casillas et al [2013].…”

Section: Introductionmentioning

confidence: 99%

Optimal coverage-based placement of static leak detection devices for pipeline water supply networks

Blanco¹,

Martínez-Antón²

2023

Preprint

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In this paper we provide a mathematical optimization based framework to determine the location of leak detection devices along a network. Assuming that the devices are endowed with a given coverage area, we analyze two different models. The first model aims to minimize the number of devices to be located in order to (fully or partially) cover the volume of the network. In the second model, the number of devices is given, and the goal is to locate them to provide maximal volume coverage. In our models it is not assumed that the devices are located in the network (nodes or edges) but in the entire space, which allow to more flexible coverage. We report the results of applying our models to real-world water supply pipeline urban network, supporting the validity of our models.

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Improving the leak detection efficiency in water distribution networks using noise loggers

Cited by 32 publications

References 45 publications

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

AI-based smart water leak detection using hydrophones

Optimal coverage-based placement of static leak detection devices for pipeline water supply networks

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Improving the leak detection efficiency in water distribution networks using noise loggers

Cited by 32 publications

References 45 publications

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

AI-based smart water leak detection using hydrophones ​

Optimal coverage-based placement of static leak detection devices for pipeline water supply networks

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Product

Resources

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AI-based smart water leak detection using hydrophones