An innovative machine learning based framework for water distribution network leakage detection and localization

Fan, Xudong; Yu, Xiong

doi:10.1177/14759217211040269

Cited by 24 publications

(7 citation statements)

References 42 publications

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“…The main aim of the proposed system was to develop an effective framework for optimizing leak detection by decreasing the cost of pressure sensor procurement and maximizing the coverage of the sensor network. Fan et al [156] and Coelho et al [157] used a machine learning-based framework for water leakage detection in their works. In the former case [151], the authors used a semisupervised learning framework of clustering and then localization for optimal sensor placement and leakage localization.…”

Section: Irrigation Leakagementioning

confidence: 99%

“…In this approach, the WDS is partitioned into water leakage zones using a modified K-means clustering algorithm and a machine learning model is trained for leakage detection. New leakage characteristics extracted by the unsupervised learning algorithms proposed in that study [156] were determined by principal component analysis and an autoencoder neural network. An important feature of the proposed model was that it could be trained with the leakage characteristics matrix of the unbalanced data to detect abnormal conditions.…”

Section: Irrigation Leakagementioning

confidence: 99%

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Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

et al. 2023

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Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

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Section: Irrigation Leakagementioning

confidence: 99%

Section: Irrigation Leakagementioning

confidence: 99%

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

et al. 2023

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“…The CtL-SSL framework is applied to two test bed WDNs and achi detection accuracy and around 83% final leak location accuracy using un with less than 10% leak data. The developed CtL-SSL framework advances tion strategy by reducing data requirements, guiding the optimal sensor p positioning leakage through the WDN leakage zone partition [60]. To impr sion and intelligence of leakage detection, [61] proposed a leakage detectio uses internal mode function, approximation entropy, and main compone build a signal feature set and uses a support vector machine (SVM) as a cl As an enabling technology, AI can reconstruct the modes of production, distribution, exchange, and consumption in the real economy, particularly in the engineering sector.…”

Section: Artificial Intelligence and Machine Learning Techniquesmentioning

confidence: 99%

“…The implemented AI/ML system promotes the idea that the earlier you detect leakage, the more likely you are to identify bursts before they happen, and that abnormal pressure behaviour in a zone can lead to bursts [59]. The study [60] used an ML technique named clustering-then-localization semi-supervised learning (CtL-SSL), which uses the topological relationship of WDN and its leakage properties for WDN partitioning and sensor placement, and then uses monitoring data for leakage detection and leakage localization. The CtL-SSL framework is applied to two test bed WDNs and achieves 95% leak detection accuracy and around 83% final leak location accuracy using unbalanced data with less than 10% leak data.…”

Section: Artificial Intelligence and Machine Learning Techniquesmentioning

confidence: 99%

Application of Software and Hardware-Based Technologies in Leaks and Burst Detection in Water Pipe Networks: A Literature Review

Joseph

Sharma

Staden

et al. 2023

Water

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With the rise of smart water cities, water resource management has become increasingly important. The increase in the use of intelligent leak detection technologies in the water, gas, oil, and chemical industries has led to a significant improvement in safety, customer, and environmental results, and management costs. The aim of this review article is to provide a comprehensive overview of the application of software and hardware-based technologies in leak detection and bursts in water pipeline networks. This review aims to investigate the existing literature on the subject and to analyse the key leak detection systems in the water industry. The novelty of this review is the comprehensive analysis of the literature on software and hardware-based technologies for leak and burst detection in water pipe networks. Overall, this review article contributes to understanding the latest developments and challenges in the application of software- and hardware-based technologies for leak and burst detection in water pipe networks, and serves as a valuable resource for researchers, engineers, and practitioners working in the field of water distribution systems.

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“…If it is an anomaly then a field investigation is initiated. 46 [94] Pressure Time series pressure S LD A framework is proposed that includes WDN leak zone partition, leak detection, and leak zone location. The architecture uses autoencoder algorithm and k-mean model.…”

Section: A Overviewmentioning

confidence: 99%

A Review on Current Technologies and Future Direction of Water Leakage Detection in Water Distribution Network

et al. 2022

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Water leakage in the supply system is a silent problem that costs billions of dollars yearly. As these supply pipes are mostly underground, this leakage remains undetected for a long time. In 2019, Liemberger and Wyatt estimated an annual loss of thirty-nine billion dollars due to water leakage in the supply pipe. In this systematic review, we have analyzed forty-seven articles about water leakage detection and location research. The aim is to find the new technology, trends, and possible direction in this research field. We have derived four research questions. The first question was about how the research evolved over time, and we have observed that researchers focus more on experimental data collection, ML algorithms, and IoT technology. The second question was about the sensor the researcher was using. The most popular sensors researchers have used are: vibration, acoustic, and flow sensors, as they are cheap and easy to install. We can also see some novel applications of image and optical fiber sensors. The third question was about the trend in the algorithm. ML and threshold-based algorithms are dominating the field. The fourth question was about the communication technology trend, and WIFI, cellular IoT, and LoRa technology are leading the space capturing 80% of the research.

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An innovative machine learning based framework for water distribution network leakage detection and localization

Cited by 24 publications

References 42 publications

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

Application of Software and Hardware-Based Technologies in Leaks and Burst Detection in Water Pipe Networks: A Literature Review

A Review on Current Technologies and Future Direction of Water Leakage Detection in Water Distribution Network

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