A Method of Leakage Location in Water Distribution Networks using Artificial Neuro-Fuzzy System

Wachla, Dominik; Przystałka, P.; Moczulski, Wojciech

doi:10.1016/j.ifacol.2015.09.692

Cited by 45 publications

(17 citation statements)

References 10 publications

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“…In [5], a method based on the use of Support Vector Machines (SVM) is proposed that analyzes data obtained by a set of pressure control sensors of a pipeline network to locate and compute the size of a possible leak present in a WDN. More recently, the use of k-Nearest Neighbors (k-NN), Bayesian and neuro-fuzzy classiers for leak localization purposes has been proposed in [6], [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

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

Soldevila¹,

Blesa

Tornil-Sin

et al. 2018

Computers & Chemical Engineering

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This paper presents a sensor placement approach for classier-based leak localization in water distribution networks. The proposed method is based on a hybrid feature selection algorithm that combines the use of a lter based on relevancy and redundancy with a wrapper based on genetic algorithms. This algorithm is applied to data generated by hydraulic simulation of the considered water distribution network and it determines the optimal location of a prespecied number of pressure sensors to be used by a leak localization method based on pressure models and classiers proposed in previous works by the authors. The method is applied to a small-size simplied network (Hanoi) to better analyze its computational performance and to a mediumsize network (Limassol) to demonstrate its applicability to larger real-size networks.

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Section: Introductionmentioning

confidence: 99%

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

Soldevila¹,

Blesa

Tornil-Sin

et al. 2018

Computers & Chemical Engineering

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show abstract

“…Recently, a mixed model-based/data-based approach has been proposed for water loss detection and location (Wachla et al, 2015;Soldevila et al, 2016;Zhang et al, 2016;Moczulski et al , 2016). These works suggest the use of a hydraulic model of the DMA for generating data sets which are then used for the calibration of a pattern recognition method.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a vast amount of data must be collected for each class given the range of possible scenarios, and if the real leakage features and network conditions differ from the simulated ones, then the fault location performance may deteriorate. Moreover, in these works the demand uncertainty is not explicitly considered, and some of them assume that multiple flow sensors are installed on the DMA (Wachla et al, 2015;Moczulski et al, 2016). Ultimately, obtaining a data set of all the possible leak scenarios is not a feasible task even for networks with a model that is available.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Approach to Leak Detection and Location in Water Distribution Networks

Quiñones-Grueiro

Verde

Prieto-Moreno

et al. 2018

International Journal of Applied Mathematics and Computer Science

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The water loss detection and location problem has received great attention in recent years. In particular, data-driven methods have shown very promising results mainly because they can deal with uncertain data and the variability of models better than model-based methods. The main contribution of this work is an unsupervised approach to leak detection and location in water distribution networks. This approach is based on a zone division of the network, and it only requires data from a normal operation scenario of the pipe network. The proposition combines a periodic transformation and a data vector extension together with principal component analysis of leak detection. A reconstruction-based contribution index is used for determining the leak zone location. The Hanoi distribution network is employed as the case study for illustrating the feasibility of the proposal. Single leaks are emulated with varying outflow magnitudes at all nodes that represent less than 2.5% of the total demand of the network and between 3% and 25% of the node’s demand. All leaks can be detected within the time interval of a day, and the average classification rate obtained is 85.28% by using only data from three pressure sensors.

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“…More recently, in [6], it has been developed a method to locate leaks using Support Vector Machines (SVM) that analyzes data obtained by a set of pressure control sensors of a pipeline network to locate and calculate the size of the leak. The use of classifiers in leak localization has been proposed in [7] and [8]. Another set of methods is based on the inverse transient analysis [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Leak localization in water distribution networks using model-based Bayesian reasoning

Soldevila

Fernandez-Canti

Blesa

et al. 2016

2016 European Control Conference (ECC)

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Abstract-This paper presents a new method for leak localization in Water Distribution Networks that uses a model-based approach combined with Bayesian reasoning. Probability density functions in model-based pressure residuals are calibrated off-line for all the possible leak scenarios by using a hydraulic simulator, being leak size uncertainty, demand uncertainty and sensor noise considered. A Bayesian reasoning is applied online to the available residuals to determine the location of leaks present in the Water Distribution Network. A time horizon method combined with the Bayesian reasoning is also proposed to improve the accuracy of the leak localization method. The Hanoi District Metered Area case study is used to illustrate the performance of the proposed approach.

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A Method of Leakage Location in Water Distribution Networks using Artificial Neuro-Fuzzy System

Cited by 45 publications

References 10 publications

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

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

An Unsupervised Approach to Leak Detection and Location in Water Distribution Networks

Leak localization in water distribution networks using model-based Bayesian reasoning

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