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Experiments based comparative evaluations of machine learning techniques for leak detection in water distribution systems
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, and flow data difference to detect leakage in water distribution networks. Different scenarios of realistic water demand in two networks from the benchmark dataset LeakDB are used. Results demonstrate that the leak detection accuracy varies between 30% and 100% depending on the experiment and the choices of algorithms and data. HIGHLIGHTS This is the first work to use a common benchmark dataset to compare four machine learning techniques used for leak detection application.; The choice of data has a significant effect on improving leak detection performances using learning algorithms.; Six experiments are proposed to offer a comprehensive comparative study of algorithms behavior adopted for leak detection.;
Experiments based comparative evaluations of machine learning techniques for leak detection in water distribution systems
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, and flow data difference to detect leakage in water distribution networks. Different scenarios of realistic water demand in two networks from the benchmark dataset LeakDB are used. Results demonstrate that the leak detection accuracy varies between 30% and 100% depending on the experiment and the choices of algorithms and data. HIGHLIGHTS This is the first work to use a common benchmark dataset to compare four machine learning techniques used for leak detection application.; The choice of data has a significant effect on improving leak detection performances using learning algorithms.; Six experiments are proposed to offer a comprehensive comparative study of algorithms behavior adopted for leak detection.;
Experiments based comparative evaluations of machine learning techniques for leak detection in water distribution systems
Maryam Kammoun (author) / Amina Kammoun (author) / Mohamed Abid (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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