Adrià Soldevila

TL;DR: This paper presents a method for leak localization in water distribution networks (WDNs) based on Bayesian classifiers, which is applied on-line to the computed residuals to determine the location of leaks in the WDN.

TL;DR: In this paper, a new method for leak localization in water distribution networks (WDNs) is proposed, where residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model, and a classifier is applied to the residuals with the aim of determining the leak location.

TL;DR: A data-driven approach based on the use of statistical classifiers working in the residual space is proposed for leak localization, trained using leak data scenarios in all the nodes of the network considering uncertainty in demand distribution, additive noise in sensors and leak magnitude.

TL;DR: This paper explores the use of deep learning for leak localization in Water Distribution Networks (WDNs) using pressure measurements by using a training data set including enough samples of all possible leak localizations, a Convolutional Neural Network can be used to learn the different pressure maps that carachterized each leak localization.

TL;DR: The proposed method is based on a hybrid feature selection algorithm that combines the use of a filter based on relevancy and redundancy with a wrapper based on genetic algorithms to determine the optimal location of a prespecified number of pressure sensors for leak localization in water distribution networks.