scholarly journals Leak location in water distribution networks based on dynamic tests and parametric identification

2003 ◽  
Author(s):  
F. Casella ◽  
L. Bascetta ◽  
C. Maffezzoni ◽  
G. Bodini
Keyword(s):  
Water Distribution ◽  
Parametric Identification ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Dynamic Tests ◽  
Leak Location

scholarly journals Leak Signature Space: An Original Representation for Robust Leak Location in Water Distribution Networks

Water ◽  
2015 ◽  
Vol 7 (12) ◽  
pp. 1129-1148 ◽  
Author(s):  
Myrna Casillas ◽  
Luis Garza-Castañón ◽  
Vicenç Puig ◽  
Adriana Vargas-Martinez
Keyword(s):  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Leak Location

scholarly journals Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

Sensors ◽  
2013 ◽  
Vol 13 (11) ◽  
pp. 14984-15005 ◽  
Author(s):  
Myrna Casillas ◽  
Vicenҫ Puig ◽  
Luis Garza-Castañón ◽  
Albert Rosich
Keyword(s):  
Genetic Algorithms ◽  
Water Distribution ◽  
Sensor Placement ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Optimal Sensor Placement ◽  
Leak Location

scholarly journals Prior Assumptions for Leak Localisation in Water Distribution Networks with Uncertainties

2021 ◽  
Author(s):  
Caroline Blocher ◽  
Filippo Pecci ◽  
Ivan Stoianov
Keyword(s):  
Water Distribution ◽  
Network Models ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Sensitivity Matrix ◽  
Leak Location ◽  
Wide Range ◽  
Coefficient Vector ◽  
Resistance Coefficients ◽  
Location Estimate

AbstractHydraulic model-based leak (burst) localisation in water distribution networks is a challenging problem due to a limited number of hydraulic measurements, a wide range of leak properties, and model and data uncertainties. In this study, prior assumptions are investigated to improve the leak localisation in the presence of uncertainties. For example, $$\ell _2$$ ℓ 2 -regularisation relies on the assumption that the Euclidean norm of the leak coefficient vector should be minimised. This approach is compared with a method based on the sensitivity matrix, which assumes the existence of only a single leak. The results show that while the sensitivity matrix method often yields a better leak location estimate in single leak scenarios, the $$\ell _2$$ ℓ 2 -regularisation successfully identifies a search area for pinpointing the accurate leak location. Furthermore, it is shown that the additional error introduced by a quadratic approximation of the Hazen-Williams formula for the solution of the localisation problem is negligible given the uncertainties in Hazen-Williams resistance coefficients in operational water network models.

scholarly journals Comparison of Classifiers for Leak Location in Water Distribution Networks

2018 ◽  
Vol 51 (24) ◽  
pp. 407-413 ◽  
Author(s):  
Marcos Quiñones-Grueiro ◽  
José M. Bernal-de Lázaro ◽  
Cristina Verde ◽  
Alberto Prieto-Moreno ◽  
Orestes Llanes-Santiago
Keyword(s):  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Leak Location

scholarly journals Integrated sensing technologies for detection and location of leaks in water distribution networks

2017 ◽  
Vol 17 (6) ◽  
pp. 1589-1601 ◽  
Author(s):  
Alaa Hawari ◽  
Mohammad Khader ◽  
Walaa Hirzallah ◽  
Tarek Zayed ◽  
Osama Moselhi
Keyword(s):  
Relative Humidity ◽  
Water Distribution ◽  
Accurate Determination ◽  
Distribution Networks ◽  
Operating Conditions ◽  
Water Distribution Networks ◽  
Ir Camera ◽  
Efficient Operation ◽  
Leak Location ◽  
The Impact

Abstract Water distribution networks (WDNs) are infrastructure systems that have high socioeconomic values, for which efficient operation and management are required to ensure minimal amounts of waste which can be represented in the form of leaks. Leak detection is considered as one of the challenges faced by municipalities operating WDNs because it either involves shutting down the system or requires using expensive equipment and technologies. In this paper, a novel noninvasive and nondestructive methodology for detecting leaks in water pipes was tested. Ground penetrating radar was used for accurate determination of pipe location, followed by infrared (IR) thermographic imaging for determining the leak location using four different operating conditions. Results were statistically analyzed using analysis of variance and pairwise comparison methods. Several factors were found to affect the accuracy of the proposed methodology in predicting the leak location, namely, the characteristics of the studied surface (i.e. emissivity), the characteristics of the surrounding environment (i.e. ambient temperature and relative humidity), and the operating conditions of the IR camera (i.e. speed and height of the camera). The results obtained in this study have also shown that under high ambient temperatures and high relative humidity conditions, a higher speed of the IR camera would reduce the impact of noise on the collected thermal contrast and therefore, would give better leak location prediction results. The tested methodology proved the flexibility of the approach and the ability of accurately predicting the leak locations under different conditions.

scholarly journals A Leak Zone Location Approach in Water Distribution Networks Combining Data-Driven and Model-Based Methods

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2924
Author(s):  
Marlon Jesús Ares-Milián ◽  
Marcos Quiñones-Grueiro ◽  
Cristina Verde ◽  
Orestes Llanes-Santiago
Keyword(s):  
Support Vector Machine ◽  
Differential Evolution ◽  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Data Driven ◽  
Support Vector ◽  
Location Task ◽  
Model Based ◽  
Leak Location

Model-based and data-driven methods are commonly used in leak location strategies in water distribution networks. This paper formulates a hybrid methodology in two stages that complements the advantages and disadvantages of data-driven and model-based strategies. In the first stage, a support vector machine multiclass classifier is used to reduce the search space for the leak location task. In the second stage, leak location task is formulated as an inverse problem, and solved using a variation of the differential evolution algorithm called topological differential evolution. The robustness of the method is tested considering measurement and varying demand uncertainty conditions ranging from 5 to 15% of node nominal demands. The performance of the hybrid method is compared to the support vector machine classifier and topological differential evolution approaches as standalone methods of leak location. The hybrid proposal shows higher performance in terms of location accuracy, zone size, and computational load.

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