scholarly journals First Results in Leak Localization in Water Distribution Networks using Graph-Based Clustering and Deep Learning

2020 ◽  
Vol 53 (2) ◽  
pp. 16691-16696
Author(s):  
Luis Romero ◽  
Joaquim Blesa ◽  
Vicenç Puig ◽  
Gabriela Cembrano ◽  
Carlos Trapiello
Keyword(s):  
Deep Learning ◽  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
First Results ◽  
Leak Localization ◽  
Graph Based Clustering

scholarly journals Leak localization in water distribution networks using classifiers with cosenoidal features

2020 ◽  
Vol 53 (2) ◽  
pp. 16697-16702
Author(s):  
I. Santos-Ruiz ◽  
J. Blesa ◽  
V. Puig ◽  
F.R. López-Estrada
Keyword(s):  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Leak Localization

scholarly journals Sensor placement for classifier-based leak localization in water distribution networks using hybrid feature selection

2018 ◽  
Vol 108 ◽  
pp. 152-162 ◽  
Author(s):  
Adrià Soldevila ◽  
Joaquim Blesa ◽  
Sebastian Tornil-Sin ◽  
Rosa M. Fernandez-Canti ◽  
Vicenç Puig
Keyword(s):  
Feature Selection ◽  
Water Distribution ◽  
Sensor Placement ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Leak Localization

scholarly journals Compressed sensing based optimal sensor placement for leak localization in water distribution networks

2017 ◽  
Vol 20 (6) ◽  
pp. 1286-1295 ◽  
Author(s):  
Xiang Xie ◽  
Quan Zhou ◽  
Dibo Hou ◽  
Hongjian Zhang
Keyword(s):  
Compressed Sensing ◽  
Knapsack Problem ◽  
Water Distribution ◽  
Sensor Placement ◽  
Real Life ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Mutual Coherence ◽  
Optimal Sensor Placement ◽  
Leak Localization

Abstract The performance of model-based leak detection and localization techniques heavily depends on the configuration of a limited number of sensors. This paper presents a sensor placement optimization strategy that guarantees sufficient diagnosability while satisfying the budget constraint. Based on the theory of compressed sensing, the leak localization problem could be transformed into acquiring the sparse leak-induced demands from the available measurements, and the average mutual coherence is devised as a diagnosability criterion for evaluating whether the measurements contain enough information for identifying the potential leaks. The optimal sensor placement problem is then reformulated as a {0, 1} quadratic knapsack problem, seeking an optimal sensor placement scheme by minimizing the average mutual coherence to maximize the degree of diagnosability. To effectively handle the complicated real-life water distribution networks, a validated binary version of artificial bee colony algorithm enhanced by genetic operators, including crossover and swap, is introduced to solve the binary knapsack problem. The proposed strategy is illustrated and validated through a real-life water distribution network with synthetically generated field data.

scholarly journals Leak localization in water distribution networks using Bayesian classifiers

2017 ◽  
Vol 55 ◽  
pp. 1-9 ◽  
Author(s):  
Adrià Soldevila ◽  
Rosa M. Fernandez-Canti ◽  
Joaquim Blesa ◽  
Sebastian Tornil-Sin ◽  
Vicenç Puig
Keyword(s):  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Bayesian Classifiers ◽  
Leak Localization

scholarly journals Leak Localization in Water Distribution Networks Using Pressure and Data-Driven Classifier Approach

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 54 ◽  
Author(s):  
Congcong Sun ◽  
Benjamí Parellada ◽  
Vicenç Puig ◽  
Gabriela Cembrano
Keyword(s):  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Data Driven ◽  
Time Step ◽  
Water Losses ◽  
Localization Accuracy ◽  
Linear Discriminant ◽  
Data Driven Approach ◽  
Leak Localization

Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses through leak detection and localization, timely and efficiently using advanced techniques is an urgent humanitarian need. There are numerous methods being used to localize water leaks in WDNs through constructing hydraulic models or analyzing flow/pressure deviations between the observed data and the estimated values. However, from the application perspective, it is very practical to implement an approach which does not rely too much on measurements and complex models with reasonable computation demand. Under this context, this paper presents a novel method for leak localization which uses a data-driven approach based on limit pressure measurements in WDNs with two stages included: (1) Two different machine learning classifiers based on linear discriminant analysis (LDA) and neural networks (NNET) are developed to determine the probabilities of each node having a leak inside a WDN; (2) Bayesian temporal reasoning is applied afterwards to rescale the probabilities of each possible leak location at each time step after a leak is detected, with the aim of improving the localization accuracy. As an initial illustration, the hypothetical benchmark Hanoi district metered area (DMA) is used as the case study to test the performance of the proposed approach. Using the fitting accuracy and average topological distance (ATD) as performance indicators, the preliminary results reaches more than 80% accuracy in the best cases.

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