# Everything You Always Wanted to Know About Urban Water Networks But Were Afraid to Ask -Extended Abstract

Nanée Chahinian, Batoul Haydar, Serge Conrad, Stéphane Debard, Carole Delenne

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Nanée Chahinian, Batoul Haydar, Serge Conrad, Stéphane Debard, Carole Delenne. Everything You Always Wanted to Know About Urban Water Networks But Were Afraid to Ask -Extended Abstract. ENIGMA 2023 - 1st Workshop on AI-driven heterogeneous data management: Completing, merging, handling inconsistencies and query-answering, co-located with 20th International Conference on Principles of Knowledge Representation and Reasoning (KR 2023), Sep 2023, Rhodes, Greece. pp.1-2. hal-04231966

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Submitted on 7 Oct 2023

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# Everything You Always Wanted to Know About Urban Water Networks But Were Afraid to Ask - Extended Abstract

Nanée Chahinian<fnref n="1" />*, Batoul Haydar<fnref n="1" />, Serge Conrad<fnref n="1" />, Stéphane Debard<fnref n="1" /> and Carole Delenne<fnref n="1" />,<fnref n="2" />

**Abstract**

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 networksS 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.

**Keywords**

Urban water networks, sewer, stormwater,

## 1. Extended abstract

Urban water networks have played a paramount role in the development of human settlements throughout history. The role of water in migration, urbanisation and the dynamics of human civilisations as well as the implications for contemporary water management are part of the 23 unsolved problems in hydrology [1].

Urban water networks include water supply and distribution networks as well as urban drainage and sewer systems that carry rain and waste to wastewater treatment plants (WWTP) and downstream water bodies. The networks consist of natural or artificial open-flow channels and pressurized conduits. The system may also include pumps, gates, weirs and detention tanks. Some authors distinguish between production and transport networks [2]. In this work we will focus mainly on stormwater and wastewater networks i.e. urban drainage and sewer systems.

For a long time, once the schemes reached a significant scale, water management was regarded as a matter for the public authorities. Indeed, large workforce and sufficient financial resources were needed to divert hydrographic networks, bring water to farmlands, evacuate the surplus

of water and maintain the canals and ditches [3]. Water management paradigms have, of course, greatly evolved throughout time. Originally, the fundamental objective was to secure water supply. Later concerns over public health extended the perimeter to sanitation and flood protection. In the past decades, a more holistic approach has been adopted to consider the entire water cycle within the city [4, 5]. Concerns about water availability in face of growing demand have also prompted gray water reuse.

The management of these networks is a complex task and could certainly benefit from advances in information technologies. However, it appears that compared to sectors like energy, healthcare or transportation, the use of Artificial Intelligence (AI)-based techniques in the water domain is less widespread [6]. The existing applications focus mostly on modelling, optimization or data mining and their encapsulation into functional Decision Support Systems is limited. According to [7], this is not related to the methods themselves but rather to the disassociation between the fields of water and computer engineering, the limited practical experience of academics, and the great inherent complexity [7]. In a recent review, [8] state that two of the limitations of Machine Learning-based surrogate models for urban water networks are the need for large amounts of data and the lack of explainability. The third one is their specificity to each study case [8].

Missing data is a common problem when working on urban water networks [9, 10]. Like other utility networks, statutory records of water networks may be incomplete and inaccurate [11]. However, knowledge is available on these networks through European and national data models [12, 13] and practice guidelines [14]. The question is then how the AI and Knowledge Representation and

ENIGMA-23, September 03-04, 2023, Rhodes, Greece

*Corresponding author.

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nanee.chahinian@ird.fr (N. Chahinian);

batoul.haydar@etu.umontpellier.fr (B. Haydar);

serge.conrad@umontpellier.fr (S. Conrad); stephane.debard@ird.fr

(S. Debard); carole.delenne@umontpellier.fr (C. Delenne)

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0000-0002-0037-5377 (N. Chahinian); 0000-0001-6683-4399

(C. Delenne)

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© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0)

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CEUR Workshop Proceedings (CEUR-WS.org)

<footnote n="1">¹HSM IRD, CNRS, Univ Montpellier, France</footnote>

<footnote n="2">²Lemon, Centre Inria Sophia Antipolis-Méditerranée, 06902 Valbonne, France</footnote>
Reasoning (KRR) communities may join forces with the water community to put forward methods and tools to address the data imperfections of urban water networks.

## Acknowledgments

This research has received support from the European Union's Horizon research and innovation programme under the MSCA-SE (Marie Skłodowska-Curie Actions Staff Exchange) grant agreement 101086252; Call: HORIZON-MSCA-2021-SE-01; Project title: STARWARS (Stormwater and Wastewater networkS heterogeneous data AI-driven 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-CE23-0004 of the French research funding agency Agence Nationale de la Recherche (ANR).

## References

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[2] C. Ocampo-Martinez, V. Puig, G. Cembrano, J. Quevedo, Application of predictive control strategies to the management of complex networks in the urban water cycle [applications of control], IEEE Control Systems 33 (2013) 15–41. URL: https://ieeexplore.ieee.org/document/6415377/. doi:10.1109/MCS.2012.2225919.

[3] J.-L. Abbé, Chapitre 1. Sociétés et drainage des zones humides, Presses universitaires du Midi, 2006, pp. 25–61. doi:10.4000/books.pumi.10656.

[4] P. M. Bach, W. Rauch, P. S. Mikkelsen, D. T. McCarthy, A. Deletic, A critical review of integrated urban water modelling – urban drainage and beyond, Environmental Modelling & Software 54 (2014) 88–107. doi:10.1016/j.envsoft.2013.12.018.

[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

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, Inferring the most probable maps of underground utilities using bayesian mapping model, Journal of Applied Geophysics 150 (2018) 52–66. URL: https://linkinghub.elsevier.com/retrieve/pii/S092698511730143X. doi:10.1016/j.jappgeo.2018.01.006.

[12] Inspire, Sewerage, 2023. URL: https://inspire.ec.europa.eu/codelist/EconomicActivityNACEValue/E.37.00.

[13] COVADIS, Standard de données Réseaux d'AEP & d'assainissement, version 1.2, 2019. URL: http://www.geoinformations.developpement-durable.gouv.fr/.

[14] City of Cape Town, Standards and guidelines for roads & stormwater, Technical Report, Urban Mobility Directorate, 2022. URL: https://resource.capetown.gov.za/documentcentre/Documents/Procedures,%20guidelines%20and%20regulations/Standards_Guidelines_Roads_and_Stormwater.pdf.