First results in leak localization in water distribution networks using graph-based clustering and deep learning
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hdl:2117/343664
Tipus de documentText en actes de congrés
Data publicació2020
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Abstract
This paper presents a methodology for the localization of leaks in water distribution networks (WDNs) by means of the combination of a deep learning (DL) approach and a graph-based clustering technique. A data set for all possible leak locations is generated from pressure measurements and utilized to feed an image encoding process based on the Gramian Angular Field (GAF) technique, hence producing an equivalent data set of images. The pressure measurements are generated through the WDN simulation engine EPANET. To accomplish the training stage, the network is iteratively segmented into clusters using the Graph Agglomerative Clustering (GAC) method, and a deep learning neural network (DLNN) is trained to correctly
indicate the leak location at one of the created clusters. The achieved neural networks tree can be traversed through its di erent branches depending on each classi cation result, until the nal cluster is reached. Consequently, leaks can be located with a success rate that grows inversely to the size of the clusters. Due to the dependency of the latter on the number of clusters, which can be settled, the presented method is adaptable to the considered network features ( as e.g. dimensions, sensors placement and accuracy) and requisites (as e.g. localization area size).
CitacióRomero, L. [et al.]. First results in leak localization in water distribution networks using graph-based clustering and deep learning. A: World Congress of the International Federation of Automatic Control. "IFAC 2020 - 21th World Congress of the International Federation of Automatic Control: Berlin, Germany". 2020, p. 1067:1-1067:6.
Col·leccions
- IRI - Institut de Robòtica i Informàtica Industrial, CSIC-UPC - Ponències/Comunicacions de congressos [576]
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos [1.500]
- SAC - Sistemes Avançats de Control - Ponències/Comunicacions de congressos [582]
- Doctorat en Automàtica, Robòtica i Visió - Ponències/Comunicacions de congressos [166]
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