A
Adrià Soldevila
Researcher at Polytechnic University of Catalonia
Publications - 13
Citations - 362
Adrià Soldevila is an academic researcher from Polytechnic University of Catalonia. The author has contributed to research in topics: Pressure sensor & Pressure measurement. The author has an hindex of 7, co-authored 13 publications receiving 226 citations. Previous affiliations of Adrià Soldevila include Spanish National Research Council.
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Journal ArticleDOI
Leak localization in water distribution networks using Bayesian classifiers
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.
Journal ArticleDOI
Leak localization in water distribution networks using a mixed model-based/data-driven approach
Adrià Soldevila,Joaquim Blesa,Sebastian Tornil-Sin,Eric Duviella,Rosa M. Fernandez-Canti,Vicenç Puig +5 more
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.
Journal ArticleDOI
Leak Localization in Water Distribution Networks using Pressure Residuals and Classifiers
Lise Ferrandez-Gamot,Pierre Busson,Joaquim Blesa,Sebastian Tornil-Sin,Vicenç Puig,Eric Duviella,Adrià Soldevila +6 more
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.
Proceedings ArticleDOI
Leak Localization in Water Distribution Networks using Deep Learning
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.
Journal ArticleDOI
Sensor placement for classifier-based leak localization in water distribution networks using hybrid feature selection
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.