Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data

Coelho, Joao; Glória, André; Sebastião, Pedro

doi:10.3390/iot1020026

Cited by 43 publications

(16 citation statements)

References 24 publications

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“…Alves Coelho et al [98] proposed a wireless sensor network-based system that detects water leaks with 75% accuracy, using an autonomous learning algorithm. The ESP32 microcontroller was used, with 32 general-purpose input/output ports, 12 analogue ports, and power efficiency.…”

Section: What Are the Current Smart Water Application Design Techniqu...mentioning

confidence: 99%

Building a Smart Water City: IoT Smart Water Technologies, Applications, and Future Directions

Okoli,

Kabaso

2024

Water

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Water is an essential service for the sustainable development and economic competitiveness of any country. The global water demand has increased substantially due to economic development, climate change, and rising population. The Internet of Things (IoT) and Information and Communication Technologies (ICT) can help conserve available water resources. Smart cities apply IoT to boost the performance and efficiency of urban facilities. Smart cities are towns created to use IoT and ICT (innovative technologies) such as smart water applications. Several studies on smart water technology have been conducted, but there is a need to review current research that leverages the IoT as a communication technology to design effective smart water applications. This review paper is aimed at presenting evidence on the current design of smart water applications. The study also covers publication statistics to increase collaboration between stakeholders. Findings show that various technologies such as microcontrollers, embedded programming languages, sensors, communication modules, and protocols are used by researchers to accomplish their aim of designing IoT-based smart water solutions. None of the publications employed the 5G mobile networks as a communication module for their smart water application development. Findings further show that the integration of 3D printing and solar energy into IoT-based smart water applications is revolutionary and can increase the sustainability of the systems. Future directions required to ensure that developed smart water applications are widely adopted to help conserve and manage water resources are suggested.

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Section: What Are the Current Smart Water Application Design Techniqu...mentioning

confidence: 99%

Building a Smart Water City: IoT Smart Water Technologies, Applications, and Future Directions

Okoli,

Kabaso

2024

Water

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“…Detailed application of such techniques can be found in existing reviews (Chan et al, 2019; El‐Zahab & Zayed, 2019). It is pertinent to mention that new technologies like micro‐electromagnetic systems (MEMS)‐based customized sensors (Xu et al, 2019), fiber‐optic‐based sensors (Ibrahim et al, 2021), wireless sensing networks (WSNs) (See et al, 2009), internet of things (IoT) (Alves Coelho et al, 2020), and satellite imagery (Chen et al, 2020) for more precise and cost‐effective leak detection are being developed. Recently, Tariq et al (2021) present a detailed review covering the application of MEMS‐based accelerometers and wireless sensor networks.…”

Section: Current Technologies For Leak Detection and Localizationmentioning

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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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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“…ANN model was used for leak detection exclusively for a water supply pipeline network and could detect leak with an accuracy of 87% and with the use of time filter, it increased to 97% (46). SVM is a supervised ML algorithm which can classify data using a hyperplane between two classes of data and creates a linear relationship (47). Both leak size and location were predicted by classifying data points based on the pattern recognized using SVM algorithm (48).…”

Section: Ecs Transactions 107 (1) 4281-4297 (2022)mentioning

confidence: 99%

Studies on Leak Detection in Process Pipelines Using Artificial Neural Networks/Machine Learning

Meda¹,

Harshitha²,

`Hulake³

et al. 2022

ECS Trans.

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In process industries, leakage in pipelines is common and an environmental, health and economic issue to be addressed without fail. To detect the presence of leaks in pipelines, there are several conventional leak detection techniques that are available. From the literature, conventional leak detection techniques can be mainly classified into: hardware-based, visual and software-based methods. Researchers have focused more on software-based methods due to simple and reliable operation. Under software-based methods, using machine learning algorithms that is a data driven approach for leak detection and localization is becoming popular because of learning capabilities. Therefore, in this review, several recent conventional leak detection methods and artificial neural networks/machine learning based leak detection methods are described. It is an attempt made to identify leak detection techniques along with the common methodology followed for building a leak detection system using artificial neural networks/machine learning.

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Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data

Cited by 43 publications

References 24 publications

Building a Smart Water City: IoT Smart Water Technologies, Applications, and Future Directions

Building a Smart Water City: IoT Smart Water Technologies, Applications, and Future Directions

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

Studies on Leak Detection in Process Pipelines Using Artificial Neural Networks/Machine Learning

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