Urban water networks have played a paramount role in the development of human settlements throughout history. Originally designed to supply water, their role has evolved to drainage and, more recently, to gray water reuse. This talk will present a brief overview of the development of urban water networks and highlight their evolution through time. The changes in design rules, data acquisition and exchange standards will be highlighted. Data imperfections will be addressed through examples drawn from the datasets acquired by the consortiums of the STARWARS (STormwAteR and WastewAteR networkS heterogeneous data AI-driven management) and CROQUIS (Collecting, Representing, cOmpleting, merging and Querying heterogeneous and UncertaIn data) projects. Some attempts made in the literature to overcome data limitations will be addressed. Rather than a traditional conclusion, the talk will attempt to incite discussions on possible ways to integrate knowledge and reasoning to address data imperfections.
Union’s Horizon research and innovation programme under the MSCA-SE (Marie Skłodowska-Curie Actions Staf Exchange) grant agreement 101086252; Call: HORIZONMSCA-2021-SE-01; Project title: STARWARS (STormwAteR and WastewAteR networkS heterogeneous data AIdriven management).
This research has also received support from the ANR CROQUIS (Collecte, représentation, complétion, fusion et interrogation de données de réseaux d’eau urbains hétérogènes et incertaines) project, grant ANR-21-CE230004 of the French research funding agency Agence Nationale de la Recherche (ANR). enhance the potential of decision support systems? a review for the urban water sector, AI Communications 29 (2016) 747–756. doi:10.3233/
AIC-160714. [8] A. Garzón, Z. Kapelan, J. Langeveld, R. Taormina,
Machine learning-based surrogate modeling for urban water networks: Review and future research directions, Water Resources Research 58 (2022).
doi:10.1029/2021WR031808. [9] Y. Belghaddar, N. Chahinian, A. Seriai, A. Begdouri, R. Abdou, C. Delenne, Graph convolutional networks: Application to database completion of wastewater networks, Water 13 (2021) 1681.
doi:10.3390/w13121681. [10] N. Chahinian, C. Delenne, B. Commandré,
M. Derras, L. Deruelle, J.-S. Bailly, Automatic mapping of urban wastewater networks based on manhole cover locations, Computers, Environment and Urban Systems 78 (2019) 101370. URL: https://linkinghub.elsevier.com/ retrieve/pii/S0198971518306379. doi:10.1016/j.
compenvurbsys.2019.101370. [11] M. Bilal, W. Khan, J. Muggleton, E. Rustighi,
H. Jenks, S. R. Pennock, P. R. Atkins, A. Cohn, [1] G. Blöschl, M. F. Bierkens, A. Chambel, C. Cud- Inferring the most probable maps of underennec, G. D. et al., Twenty-three unsolved prob- ground utilities using bayesian mapping lems in hydrology (UPH)-a community perspective, model, Journal of Applied Geophysics Hydrological sciences journal 64 (2019) 1141–1158. 150 (2018) 52–66. URL: https://linkinghub. doi:10.1080/02626667.2019.1620507. elsevier.com/retrieve/pii/S092698511730143X. [2] C. Ocampo-Martinez, V. Puig, G. Cembrano, doi:10.1016/j.jappgeo.2018.01.006.
J. Quevedo, Application of predictive control strate- [12] Inspire, Sewerage, 2023. URL: https://inspire.ec. gies to the management of complex networks in europa.eu/codelist/EconomicActivityNACEValue/ the urban water cycle [applications of control], E.37.00.
IEEE Control Systems 33 (2013) 15–41. URL: https: [13] COVADIS, Standard de données Réseaux //ieeexplore.ieee.org/document/6415377/. doi:10. d’AEP & d’assainissement, version 1.2, 1109/MCS.2012.2225919. 2019. URL: http://www.geoinformations. [3] J.-L. Abbé, Chapitre 1. Sociétés et drainage des zones developpement-durable.gouv.fr/. humides, Presses universitaires du Midi, 2006, pp. [14] City of Cape Town, Standards and guide25–61. doi:10.4000/books.pumi.10656. lines for roads & stormwater, Technical [4] P. M. Bach, W. Rauch, P. S. Mikkelsen, D. T. Mc- Report, Urban Mobility Directorate, 2022. carthy, A. Deletic, A critical review of integrated ur- URL: https://resource.capetown.gov.za/ ban water modelling – urban drainage and beyond, documentcentre/Documents/Procedures, Environmental Modelling & Software 54 (2014) 88– %20guidelines%20and%20regulations/Standards_ 107. doi:10.1016/j.envsoft.2013.12.018. Guidelines_Roads_and_Stormwater.pdf. [5] R. R. Brown, N. Keath, T. H. Wong, Urban water management in cities: historical, current and future regimes, Water Science and Technology 59 (2009) 847–855. doi:10.2166/WST.2009.029. [6] N. Doorn, Artificial intelligence in the water domain: Opportunities for responsible use, Science of The Total Environment 755 (2021) 142561.
doi:10.1016/j.scitotenv.2020.142561. [7] A. Hadjimichael, J. Comas, L. Corominas, Do
machine learning methods used in data mining