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

Probabilistic Minimum Night Flow Estimation in Water Distribution Networks and Comparison with the Water Balance Approach: Large-Scale Application to the City Center of Patras in Western Greece

“…It is important to note that all 4 PMAs did not exhibit any prolonged periods of malfunctioning and/or pressure regulation issues. Data were, first, quality assessed in order to remove any errors related to data transmission system malfunctions (i.e., communication glitches of the 3G transmission system [42]). Table 1.…”

“…The minimum night flow (MNF) method, which is the most popular approach for RL estimation in WDNs, is commonly applied in district metered areas (DMAs, hydraulic isolated areas/zones of a WDN, see [40]) or pressure management areas (PMAs, i.e., DMAs where pressure management is applied) under the assumption that human activity during the late night and early morning hours is minimal [5,41]. Therefore, MNF estimates can be considered representative of background losses, defined as the sum of small and possibly undetectable leaks, the localization and repair of which is deemed economically unprofitable [42], unless the water loss is gradually increased to the point where it is possible to detect and repair them in a cost-effective setting [43,44].…”

“…For example, depending on the application and the networks' monitoring characteristics, extraction of night flow minima may start (end) at different night (morning) hours, usually ignoring the resolution of the data and the seasonality of the consumption [5,31,[45][46][47][48][49][50][51][52][53]. To bridge this gap, in a recent study, Serafeim et al [42] developed two conceptually different probabilistic approaches for MNF estimation, which are based on statistical metrics, and are suited to minimize noise effects, allowing for more accurate representation of the low flows during night hours, as well as confidence interval estimation of the observed MNFs, making them more suitable for practical applications.…”

“…The purpose of this work is to compare the effectiveness and robustness of WL estimation approaches found in the international literature, based on a large-scale, realworld application in selected PMAs of the WDN of the city of Patras, the third largest city in Greece. Since there are no available burst records for the WDN of the city of Patras (required for effective application of BABE method) and, also, its sewer network is combined, comparison is limited to: (a) the water balance or top-down approach introduced by IWA, and (b) the bottom-up approach based on MNF estimates obtained by applying the probabilistic approach for MNF estimation by Serafeim et al [42].…”

Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms

“…To extensively compare the selected quantile regression algorithms (see Table 1), we use a large urban water flow data set comprised by recent measurements (taken during 2015–2020) from 54 local automated stations (hereafter referred to as “gauges”) located at the inlet points of individual district metered areas of the water distribution network of the city of Patras in Western Greece (see Figure 1). The district metered areas exhibit various sizes, topographic and network specific characteristics, as well as data availability (see Serafeim et al., 2022). These gauges are part of the “Integrated System for Pressure Management, Remote Operation and Leakage Control of the Water Distribution Network of the City of Patras”, which is the largest smart water network in Greece, with the Municipal Enterprise of Water Supply and Sewerage of the City of Patras (DEYAP) acting as the competent Authority for its operation and management (Karathanasi & Papageorgakopoulos, 2016).…”