Graph Convolutional Networks: Application to Database Completion of Wastewater Networks
Autor: | Nanée Chahinian, Reda Abdou, Yassine Bel-Ghaddar, Abderrahmane Seriai, Ahlame Begdouri, Carole Delenne |
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Přispěvatelé: | Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoires Systèmes Intelligents et Applications (LSIA), Université Sidi Mohamed Ben Abdellah (USMBA), Berger-Levrault, Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Service (systems architecture)
Exploit Computer science Process (engineering) Geography Planning and Development graph neural network Topology (electrical circuits) 02 engineering and technology [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Aquatic Science computer.software_genre Biochemistry [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 020204 information systems 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology TD201-500 Water Science and Technology Structure (mathematical logic) Water supply for domestic and industrial purposes Database Hydraulic engineering Missing data missing value imputation 6. Clean water machine learning Wastewater Graph (abstract data type) 020201 artificial intelligence & image processing wastewater network TC1-978 computer |
Zdroj: | Water Volume 13 Issue 12 Water, Vol 13, Iss 1681, p 1681 (2021) Water, 2021, 13 (12), pp.1681. ⟨10.3390/w13121681⟩ Water, MDPI, 2021, 13 (12), pp.1681. ⟨10.3390/w13121681⟩ |
ISSN: | 2073-4441 |
DOI: | 10.3390/w13121681 |
Popis: | International audience; 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. |
Databáze: | OpenAIRE |
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