Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Water

Abderrahmane Seriai¹  Reda Abdou¹  Carole Delenne²  Yassine Belghaddar²  Nanee Chahinian²  Ahlame Begdouri³ 

[1] Berger-Levrault, 34470 Pérols, France;HSM, University Montpellier, CNRS, IRD, 34000 Montpellier, France;LSIA, University Sidi Mohamed Ben Abdellah, Fez 30000, Morocco;

关键词: graph neural network;    missing value imputation;    wastewater network;    machine learning;   

DOI  :  10.3390/w13121681

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown