@article{fdi:010082254,
  title = {{G}raph convolutional networks : application to database completion of wastewater networks},
  author = {{B}elghaddar, {Y}. and {C}hahinian, {N}an{\'e}e and {S}eriai, {A}. and {B}egdouri, {A}. and {A}bdou, {R}. and {D}elenne, {C}.},
  editor = {},
  language = {{ENG}},
  abstract = {{W}astewater networks are mandatory for urbanisation. {T}heir management, including the prediction and planning of repairs and expansion operations, requires precise information on their underground components (manhole covers, equipment, nodes, and pipes). {H}owever, 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. {T}his 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. {T}herefore, databases related to wastewater networks may suffer from missing data. {T}o 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. {O}ur results show that {G}raph {C}onvolutional {N}etwork ({GCN}) models yield better results than classical methods and represent a useful tool for missing data completion.},
  keywords = {graph neural network ; missing value imputation ; wastewater network ; machine learning},
  booktitle = {},
  journal = {{W}ater},
  volume = {13},
  numero = {12},
  pages = {1681 [19 ]},
  year = {2021},
  DOI = {10.3390/w13121681},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082254},
}