Related Papers
Proceedings of the International Conference on Evolving Cities
Machine-Learning-Based Health Monitoring and Leakage Management of Water Distribution Systems
2023 •
Liz Varga
Intelligent Detection and Prediction Methods of Water Leakage: Systematic Literature Review
Ahmed Warad
the challenge of Leakage forecasting from pipelines has the potential to cause significant environmental damage and economic losses. While pipelines are designed and constructed to maintain their integrity, it is difficult to avoid the occurrence of leakage in a pipeline system during its lifetime. This review paper presents Systematic reviews of relevant published studies related to topics in machine learning (ML) and data analytic (DA) technologies, specifically, concerning distributed Water network leakage and Leakage forecasting methods from pipelines, published in the last nine years (2014-2022). This paper has been motivated by the lack of Systematic literature review articles that integrate water network failure and leakage modeling. Some of the existing practices reviewed the pipe condition and its failure. Others focused on the prediction models, whereas the rest outlined failure prediction models of large diameter mains only. The mainstream of the current practice, highlighted in this paper characterizes the structural deterioration and failure rates using various machine learning techniques, whereas the remainder of research covers a multiplying of machine learning methods to forecast and model the pipeline risk of failure. On top of this, this paper includes a fundamental understanding of the operating principles of currently available pipeline leak detection and prediction technologies. The review offers the models together with their proposed methodologies and algorithms, contributions and limitations and types of collected data used to develop the models using the systematic review method. Finally, future work and research challenges are recommended to assist the research community in setting a clear agenda for the upcoming research. To do so, we have conducted a systematic literature review of existing scientific researchers and defined a research agenda for the near future based on the findings and limitation identified in the literature Keywords
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks
2018 •
Avishek kumar
Smart cities
Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems
2021 •
Isam Shahrour, Neda MN
This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters.
Water Supply
Experiments based comparative evaluations of machine learning techniques for leak detection in water distribution systems
Amina Kammoun
Leakage in water distribution systems is a significant long-standing problem due to the huge economic and ecological losses. Different leak detection studies have been examined in literature using different types of technologies and data. Currently, although machine learning techniques have achieved tremendous progress in outlier detection approaches, they are still limited in terms of water leak detection applications. This research aims to improve the leak detection performances by refining the choices of learning data and techniques. From this perspective, commonly used techniques for leak detection are assessed in this paper, and the characteristics of hydraulic data are investigated. Four intelligent algorithms are compared, namely k-nearest neighbors, support vector machines, logistic regression, and multi-layer perceptron. This study focuses on six experiments based on identifying outliers in various packages of pressure and flow data, yearly data, seasonal data, night data, ...
A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids
stefano squartini, Marco fa*giani
In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS) algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and One-Class Support Vector Machine (OC-SVM), are adopted, under a comparative perspective. Both residential and office building environments are investigated by means of two datasets. One is the Almanac of Minutely Power dataset (AMPds), and it provides water and gas data consumption at 1, 10 and 30 min of time resolution; the other is the Department of International Development (DFID) dataset, and it provides water and gas data consumption at 30 min of time resolution. The achieved performance, computed by means of the Area Under the Curve (AUC), reaches 90% in the office building case study, thus confirming the suitability of the proposed approach for applications in smart water and gas grids.
Indian journal of science and technology
An Experimental Study to Locate Leakage in the Water Distribution Network using Real-time Wireless Sensor and Machine Learning Algorithm
2022 •
dipesh dalal
2016 IEEE Congress on Evolutionary Computation (CEC)
Exploiting temporal features and pressure data for automatic leakage detection in smart water grids
2016 •
Marco fa*giani, stefano squartini, Roberto Bonfigli
In this paper, the unsupervised approach recently proposed by the authors for automatic leakage detection in smart water grids is extended. First of all, the EPANET tool is adopted in order to simulate more realistic leakages. Also, with respect to the original work, an additional time resolution, of 30 minutes, is included, based on the water dataset of the Almanac of Minutely Power Dataset (AMPds). New experiments are performed, as well, to evaluate the results of the application of both temporal features and pressure data. The pressure data is obtained by means of the EPANEt tool, whereas the leakages are induced at run-time for a more realistic behaviour. Two alternative sets of temporal features are evaluated by combining them with the features extracted from both flow and pressure data. Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and One-Class Support Vector Machine (OC-SVM) are used to characterize the normal data behaviour, under a comparative perspective. A feature selection strategy is adopted in computer simulations and the resulting performance indices are evaluated in terms of Area Under Curve (AUC). The obtained results show that the introduction of the temporal information produces a slight performance improvement for both flow and pressure data, but, most importantly, the combination of flow and pressure features allows a significant improvement of leakage detection for both GMM and HMM at every resolution, up to 88% of AUC.
IEEE Access
Neural Network techniques for detecting intra-domestic water leaks of different magnitude
Riccardo Zese
IoT
Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data
Pedro Sebastião
Water is a crucial natural resource, and it is widely mishandled, with an estimated one third of world water utilities having loss of water of around 40% due to leakage. This paper presents a proposal for a system based on a wireless sensor network designed to monitor water distribution systems, such as irrigation systems, which, with the help of an autonomous learning algorithm, allows for precise location of water leaks. The complete system architecture is detailed, including hardware, communication, and data analysis. A study to discover the best machine learning algorithm between random forest, decision trees, neural networks, and Support Vector Machine (SVM) to fit leak detection is presented, including the methodology, training, and validation as well as the obtained results. Finally, the developed system is validated in a real-case implementation that shows that it is able to detect leaks with a 75% accuracy.
