Generalizing multi-reward functions aimed at identifying the best locations to install flow control devices in sewer systems

ABSTRACT

Multi-criteria decision models have been recently implemented in many urban flood risk management schemes due to the increasing demand of low-cost and reliable solutions to prevent and control overflow from sewer systems. Based on specific targets, these decision models encompass multiple reward (or objective) functions according to socio-economic requirements, vulnerability assessments and physical constraints on existing sewer systems. In this study, we present a simple algorithm to generalize reward functions aimed at locating the best sites to install in-sewer flow control devices, and thus controlling sewer overflow. The algorithm uses the mathematical structure of reward functions previously established, and adjust them based on hydraulic simulation results of two real sewer networks. Our results indicate that a single generalized reward function can efficiently identify strategic locations to guarantee (i) maximum in-sewer storage, (ii) minimum impact of possible flow control device failures and (iii) maximum flow discharge reduction upstream flood-prone urban areas.

KEYWORDS:

• Urban flood control
• in-sewer flow control device
• multi-criteria decision models
• sewer overflow
• reward function
• Spearman rank correlation coefficient

Acknowledgements

This research could not have been successfully achieved without the support of Águas de Coimbra (Eng. Luís Costa) and Câmara Municipal de Lisboa (Eng. Maria Helena Bicho from the Direção Municipal de Projetos e Obras). The authors extensively thank their collaboration on providing the sewer networks used in this study. Likewise, the authors would like to thank the CENTAUR project partners for their advice and contribution during the development of this study.

Author contributions

David F. Muñoz developed and implemented the model algorithm, analyzed the results and wrote the manuscript. Luís M. de Sousa and Lucas Maluf contributed to the design of the model and analysis of the results. Nuno E. Simões, João P. Leitão and Alfeu Sá Marques contributed to the manuscript writing, development of the model, discussion and interpretation of the results. All authors copy-edited the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The present research work was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 641931 [CENTAUR].