Bel-Ghaddar Y., Seriai A., Begdouri A., Delenne C., Chahinian Nanée, Derras M. (2021). Combining model-driven engineering and sewerage networks : towards a generic representation. In :
El Mohajir M. (ed.), Al Achhab M. (ed.), El Mohajir B.E. (ed.), Kwintiana Ane B. (ed.), Jellouli I. (ed.). 6th IEEE Congress on Information Science and Technology (CIST). Piscataway : IEEE, 48-53. CIST.Congress on Information Science and Technology, 6., Agadir ; Essaouira (MAR), 2021/06/05-12. ISBN 978-1-7281-6646-9.
Titre du document
Combining model-driven engineering and sewerage networks : towards a generic representation
Année de publication
2021
Auteurs
Bel-Ghaddar Y., Seriai A., Begdouri A., Delenne C., Chahinian Nanée, Derras M.
In
El Mohajir M. (ed.), Al Achhab M. (ed.), El Mohajir B.E. (ed.), Kwintiana Ane B. (ed.), Jellouli I. (ed.), 6th IEEE Congress on Information Science and Technology (CIST)
Source
Piscataway : IEEE, 2021,
48-53 ISBN 978-1-7281-6646-9
Colloque
CIST.Congress on Information Science and Technology, 6., Agadir ; Essaouira (MAR), 2021/06/05-12
Representing and processing digital data related to underground networks, particularly sewerage networks, is increasingly becoming a priority for the managers of these networks. Indeed, better representation would allow them, among others, to improve knowledge and to take the best decisions regarding these generally poorly identified infrastructures. The heterogeneity of data and the multiplicity of data models representing sewerage networks, often specific to each operator, as well as the imperfections associated with both the available data and those collected from different sources, generate complexity in terms of on-the-field interventions'efficiency. They also highlight the need for aggregation (unification), control and analysis. The main objective of our work is to merge multi-source data to obtain more precise and complete digital maps of sewerage networks. In this paper, we propose a generic data modelling for data fusion purposes taking into consideration the uncertainty aspects related to the collected data by allowing a confidence value for each data source and for each single data provided by a source.
Plan de classement
Evaluation et gestion des ressources en eau [062EVAEAU]
;
Intelligence artificielle [122INTAR]
Localisation
Fonds IRD [F B010084171]
Identifiant IRD
fdi:010084171
