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Water Quality Sensor Placement: A Multi-Objective and Multi-Criteria Approach

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Abstract

To satisfy their main goal, namely providing quality water to consumers, water distribution networks (WDNs) need to be suitably monitored. Only well designed and reliable monitoring data enables WDN managers to make sound decisions on their systems. In this belief, water utilities worldwide have invested in monitoring and data acquisition systems. However, good monitoring needs optimal sensor placement and presents a multi-objective problem where cost and quality are conflicting objectives (among others). In this paper, we address the solution to this multi-objective problem by integrating quality simulations using EPANET-MSX, with two optimization techniques. First, multi-objective optimization is used to build a Pareto front of non-dominated solutions relating contamination detection time and detection probability with cost. To assist decision makers with the selection of an optimal solution that provides the best trade-off for their utility, a multi-criteria decision-making technique is then used with a twofold objective: 1) to cluster Pareto solutions according to network sensitivity and entropy as evaluation parameters; and 2) to rank the solutions within each cluster to provide deeper insight into the problem when considering the utility perspectives.The clustering process, which considers features related to water utility needs and available information, helps decision makers select reliable and useful solutions from the Pareto front. Thus, while several works on sensor placement stop at multi-objective optimization, this work goes a step further and provides a reduced and simplified Pareto front where optimal solutions are highlighted. The proposed methodology uses the NSGA-II algorithm to solve the optimization problem, and clustering is performed through ELECTRE TRI. The developed methodology is applied to a very well-known benchmarking WDN, for which the usefulness of the approach is shown. The final results, which correspond to four optimal solution clusters, are useful for decision makers during the planning and development of projects on networks of quality sensors. The obtained clusters exhibit distinctive features, opening ways for a final project to prioritize the most convenient solution, with the assurance of implementing a Pareto-optimal solution.

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Acknowledgments

The prose of this paper has been revised by John Rawlins (a qualified member of the UK Institute of Translation and Interpreting).

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Authors and Affiliations

  1. Department de Engenharia Hidráulica e Recursos Hídricos, Federal University of Minas Gerais, CEP 31270-901, Belo Horizonte, Brazil

    Bruno Brentan, Daniel Barros & Gustavo Meirelles

  2. Department of Decision-Making Theory, Institute of Information Theory and Automation, Czech Academy of Sciences, 18208, Prague, Czech Republic

    Silvia Carpitella

  3. Dipartimento di Ingegneria, Università degli Studi di Palermo, 90128, Palermo, Italy

    Antonella Certa

  4. Fluing – Institute for Multidisciplinary Mathematics, Universitat Politècnica de València, 46022, Valencia, Spain

    Joaquín Izquierdo

Authors
  1. Bruno Brentan
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  2. Silvia Carpitella
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  4. Gustavo Meirelles
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  5. Antonella Certa
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  6. Joaquín Izquierdo
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Contributions

Conceptualization: [Bruno Brentan, Silvia Carpitella]; Methodology: [Bruno Brentan, Silvia Carpitella, Daniel Barros] ; Formal analysis and investigation: [Antonella Certa, Joaquin Izquierdo, Gustavo Meirelles ]; Writing - original draft preparation: [Bruno Brentan, Silvia Carpitella Joaquin Izquierdo,]; Writing - review and editing: [Gustavo Meirelles, Daniel Barros, Antonella Certa]; Supervision: [Gustavo Meirelles, Antonella Certa, Joaquin Izquierdo]

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Correspondence to Bruno Brentan.

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Brentan, B., Carpitella, S., Barros, D. et al. Water Quality Sensor Placement: A Multi-Objective and Multi-Criteria Approach. Water Resour Manage 35, 225–241 (2021). https://doi.org/10.1007/s11269-020-02720-3

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