Probabilistic estimation of minimum night flow in water distribution networks: large-scale application to the city of Patras in western Greece

We introduce two alternative probabilistic approaches for minimum night flow (MNF) estimation in water distribution networks (WDNs), which are particularly suited to minimize noise effects, allowing for a better representation of the low flows during night hours, as well as the overall condition of the network. The strong point of both approaches is that they allow for confidence interval estimation of the observed MNFs. The first approach is inspired by filtering theory, and proceeds by identifying a proper scale for temporal averaging to filter out noise effects in the obtained MNF estimates. The second approach is more intuitive, as it estimates MNF as the average flow of the most probable low-consumption states of the night flows. The efficiency of the developed methods is tested in a large-scale real world application, using flow-pressure data at 1-min temporal resolution for a 4-monthly winter period (i.e. November 2018–February 2019) from the water distribution network of the City of Patras (i.e. the third largest city in Greece). Patras’ WDN covers an area of approximately 27 km2, consists of 700 km of pipeline serving approximately 213,000 consumers, and includes 86 Pressure Management Areas (PMAs) equipped with automated local stations for pressure regulation. Although conceptually and methodologically different, the two probabilistic approaches lead to very similar results, substantiating the robustness of the obtained findings from two independent standpoints, making them suitable for engineering applications and beyond.