Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing (2024)

Corzo, C. M., Alfonso, L., Corzo, G. (2023). Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing. Journal of Water Resources Planning and Management, 149(12), Article 04023066. https://doi.org/10.1061/JWRMD5.WRENG-6005

Corzo, Clara Maria ; Alfonso, Leonardo ; Corzo, Gerald et al. / Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing. In: Journal of Water Resources Planning and Management. 2023 ; Vol. 149, No. 12.

@article{6bdded873c77488ab2c69e792fb0221e,

title = "Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing",

abstract = "Water utilities are urged to decrease their real water losses, not only to reduce costs but also to assure long-term sustainability. Hardware- and software-based techniques have been broadly used to locate leaks; within the latter, previous works that have used data-driven models mostly focused on single leaks. This paper presents a methodology to locate multiple leaks in water distribution networks employing pressure residuals. It consists of two phases: one is to produce training data for the data-driven model and cluster the nodes based on their leak-flow-rate-independent signatures using an adapted hierarchical agglomerative algorithm; the second is to locate the leaks using a top-down approach. To identify the leaking clusters and nodes, we employed a custom-built k-nearest neighbor (k-NN) algorithm that compares the test instances with the generated training data. This instance-to-instance comparison requires substantial computational resources for classification, which was overcome by the use of high-performance computing. The methodology was applied to a real network located in a European town, comprising 144 nodes and a total length of pipes of 24 km. Although its multiple inlets add redundancy to the network increasing the challenge of leak location, the method proved to obtain acceptable results to guide the field pinpointing activities. Nearly 70% of the areas determined by the clusters were identified with an accuracy of over 90% for leak flows above 3.0 L/s, and the leaking nodes were accurately detected over 50% of the time for leak flows above 4.0 L/s.",

author = "Corzo, {Clara Maria} and Leonardo Alfonso and Gerald Corzo and Dimitri Solomatine",

year = "2023",

doi = "10.1061/JWRMD5.WRENG-6005",

language = "English",

volume = "149",

journal = "Journal of Water Resources Planning and Management",

issn = "0733-9496",

publisher = "American Society of Civil Engineers (ASCE)",

number = "12",

}

Corzo, CM, Alfonso, L, Corzo, G 2023, 'Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing', Journal of Water Resources Planning and Management, vol. 149, no. 12, 04023066. https://doi.org/10.1061/JWRMD5.WRENG-6005

Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing. / Corzo, Clara Maria; Alfonso, Leonardo; Corzo, Gerald et al.
In: Journal of Water Resources Planning and Management, Vol. 149, No. 12, 04023066, 2023.

Research output: Contribution to journal › Article › Scientific › peer-review

TY - JOUR

T1 - Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing

AU - Corzo, Clara Maria

AU - Alfonso, Leonardo

AU - Corzo, Gerald

AU - Solomatine, Dimitri

PY - 2023

Y1 - 2023

N2 - Water utilities are urged to decrease their real water losses, not only to reduce costs but also to assure long-term sustainability. Hardware- and software-based techniques have been broadly used to locate leaks; within the latter, previous works that have used data-driven models mostly focused on single leaks. This paper presents a methodology to locate multiple leaks in water distribution networks employing pressure residuals. It consists of two phases: one is to produce training data for the data-driven model and cluster the nodes based on their leak-flow-rate-independent signatures using an adapted hierarchical agglomerative algorithm; the second is to locate the leaks using a top-down approach. To identify the leaking clusters and nodes, we employed a custom-built k-nearest neighbor (k-NN) algorithm that compares the test instances with the generated training data. This instance-to-instance comparison requires substantial computational resources for classification, which was overcome by the use of high-performance computing. The methodology was applied to a real network located in a European town, comprising 144 nodes and a total length of pipes of 24 km. Although its multiple inlets add redundancy to the network increasing the challenge of leak location, the method proved to obtain acceptable results to guide the field pinpointing activities. Nearly 70% of the areas determined by the clusters were identified with an accuracy of over 90% for leak flows above 3.0 L/s, and the leaking nodes were accurately detected over 50% of the time for leak flows above 4.0 L/s.

AB - Water utilities are urged to decrease their real water losses, not only to reduce costs but also to assure long-term sustainability. Hardware- and software-based techniques have been broadly used to locate leaks; within the latter, previous works that have used data-driven models mostly focused on single leaks. This paper presents a methodology to locate multiple leaks in water distribution networks employing pressure residuals. It consists of two phases: one is to produce training data for the data-driven model and cluster the nodes based on their leak-flow-rate-independent signatures using an adapted hierarchical agglomerative algorithm; the second is to locate the leaks using a top-down approach. To identify the leaking clusters and nodes, we employed a custom-built k-nearest neighbor (k-NN) algorithm that compares the test instances with the generated training data. This instance-to-instance comparison requires substantial computational resources for classification, which was overcome by the use of high-performance computing. The methodology was applied to a real network located in a European town, comprising 144 nodes and a total length of pipes of 24 km. Although its multiple inlets add redundancy to the network increasing the challenge of leak location, the method proved to obtain acceptable results to guide the field pinpointing activities. Nearly 70% of the areas determined by the clusters were identified with an accuracy of over 90% for leak flows above 3.0 L/s, and the leaking nodes were accurately detected over 50% of the time for leak flows above 4.0 L/s.

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U2 - 10.1061/JWRMD5.WRENG-6005

DO - 10.1061/JWRMD5.WRENG-6005

M3 - Article

AN - SCOPUS:85173686173

SN - 0733-9496

VL - 149

JO - Journal of Water Resources Planning and Management

JF - Journal of Water Resources Planning and Management

IS - 12

M1 - 04023066

ER -

Corzo CM, Alfonso L, Corzo G, Solomatine D. Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing. Journal of Water Resources Planning and Management. 2023;149(12):04023066. doi: 10.1061/JWRMD5.WRENG-6005