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Article

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

1
HSM, University Montpellier, CNRS, IRD, 34000 Montpellier, France
2
LSIA, University Sidi Mohamed Ben Abdellah, Fez 30000, Morocco
3
Berger-Levrault, 34470 Pérols, France
4
Lemon, Centre Inria Sophia Antipolis-Méditerranée, 06902 Valbonne, France
*
Author to whom correspondence should be addressed.
Academic Editor: Zacharias Frontistis
Water 2021, 13(12), 1681; https://doi.org/10.3390/w13121681
Received: 10 May 2021 / Revised: 11 June 2021 / Accepted: 13 June 2021 / Published: 17 June 2021
Wastewater networks are mandatory for urbanisation. Their management, including the prediction and planning of repairs and expansion operations, requires precise information on their underground components (manhole covers, equipment, nodes, and pipes). However, due to their years of service and to the increasing number of maintenance operations they may have undergone over time, the attributes and characteristics associated with the various objects constituting a network are not all available at a given time. This is partly because (i) the multiple actors that carry out repairs and extensions are not necessarily the operators who ensure the continuous functioning of the network, and (ii) the undertaken changes are not properly tracked and reported. Therefore, databases related to wastewater networks may suffer from missing data. To overcome this problem, we aim to exploit the structure of wastewater networks in the learning process of machine learning approaches, using topology and the relationship between components, to complete the missing values of pipes. Our results show that Graph Convolutional Network (GCN) models yield better results than classical methods and represent a useful tool for missing data completion. View Full-Text
Keywords: graph neural network; missing value imputation; wastewater network; machine learning graph neural network; missing value imputation; wastewater network; machine learning
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MDPI and ACS Style

Belghaddar, Y.; Chahinian, N.; Seriai, A.; Begdouri, A.; Abdou, R.; Delenne, C. Graph Convolutional Networks: Application to Database Completion of Wastewater Networks. Water 2021, 13, 1681. https://doi.org/10.3390/w13121681

AMA Style

Belghaddar Y, Chahinian N, Seriai A, Begdouri A, Abdou R, Delenne C. Graph Convolutional Networks: Application to Database Completion of Wastewater Networks. Water. 2021; 13(12):1681. https://doi.org/10.3390/w13121681

Chicago/Turabian Style

Belghaddar, Yassine, Nanee Chahinian, Abderrahmane Seriai, Ahlame Begdouri, Reda Abdou, and Carole Delenne. 2021. "Graph Convolutional Networks: Application to Database Completion of Wastewater Networks" Water 13, no. 12: 1681. https://doi.org/10.3390/w13121681

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