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An accurate estimation of residential end uses of water is helpful in developing efficient water systems. If not obtainable through direct metering, this information can be gathered by disaggregating and classifying household-level water-use data. However, most automated techniques require fine-resolution data (e.g., 1 s) and end-use parameters which may be unavailable to water utilities. To fill the above gap, this study presents a method for the automated disaggregation and classification of indoor water-use data collected at the 1-min temporal resolution, and by exclusively relying on the end-use parameter values available in the literature. Specifically, the features of each water-use event detected at the household level are compared against the most common event features for the selected end-use category. The results obtained by testing the method with real data collected at 14 households in two different countries (Italy and the Netherlands) confirm its potential in disaggregating and classifying water end-use events with an average accuracy higher than 90% and an average (normalized) root-mean-square lower than 0.06 despite the lack of information about end uses in individual households. This demonstrates that end-use detection is possible even with data whose resolution is closer to that of most commercial water meters. HIGHLIGHTS An enhanced method for water end-use classification is presented.; The method processes household water-use data collected at the 1-min resolution.; Method performance is evaluated with data collected in different spatial contexts.; General, literature-based, end-use parameter values are applied to test the method.; Method average accuracy is demonstrated to be higher than 90%.;

Anomalous water-consumption events (AEs) can significantly impact the functioning of water distribution networks, and their prompt identification can improve the service provided by water utilities. This study proposes a new methodology for AE detection and pre-localization in water distribution networks relying exclusively on pressure-data collected in the field, which are exploited to evaluate differential-pressure trends for all possible pressure-sensors couples located in the WDN. In greater detail, AEs are detected and pre-localized by analysing differential-pressure trends over time. The level of deviation of these trends from the standard is considered to provide information about (i) AE alert levels and (ii) the area of the network where the AE is most likely to occur. The application of the methodology to two real case studies featuring different characteristics in terms of residential and industrial users demonstrated method effectiveness in detecting and pre-localizing individual and simultaneous AEs of different magnitude and occurring at different times of the day, providing useful information about the presence of AEs without the need for hydraulic models, and allowing the evaluation of their effects in terms of piezometric head alteration in the different areas of the system.

Recent studies point out that water distribution networks can be affected by long- and short-term pressure oscillations due to the users' activity. However, these transients, generated at the household level, before reaching the water distribution network pass through, and thus affect, the water service line and can contribute to its deterioration. Despite the role of user-induced transients in stressing service lines, few studies in the literature explored the topic, exclusively by means of laboratory tests. The current study is aimed at exploring the effects of user's activity on a real service line starting from the field monitoring of pressure data at 500-Hz temporal resolution. Pressure signals are collected both when activating single water devices of the user supplied by the service line and during the ordinary use of domestic devices. The analyses of the acquired data highlight that the domestic service line is subjected to significant pressure variations (which can reach extreme values of −15 and +65 m) based on the device type and distance between the device and the service line and that the use of these devices can continuously stress the water service line. HIGHLIGHTS Analysis of user-induced pressure variations on a real water service line supplying a domestic user.; Characterization of single manoeuvres executed at domestic devices within the user based on a high-frequency pressure monitoring.; Continuous high-frequency pressure monitoring aimed at studying the effect of the ordinary activity of both the user supplied by the service line and the nearby users.;

In the water industry, an accurate estimation of end-use water consumption is helpful for the implementation of efficient water systems and water-saving technologies. This study aimed to explore the characteristics of water consumption at nine households north of Amsterdam (the Netherlands), subjected to water consumption monitoring at high temporal resolution (i.e. 1 s). Overall, 36,297 water-use events monitored over about 447 days were automatically segmented into 44,115 individual events by means of a new rule-based filtering algorithm, and then labelled by expert analysts. A multi-stage analysis was then conducted in order to evaluate daily per capita end-use water consumption, daily end-use profiles, average end-use parameter average, and their statistical distributions. The results achieved provide insight into the features of end-use consumption, confirming that the largest components are typically related to showers/bathtubs, toilets, and washing machines, whereas different end-use parameter distributions can emerge.

Providing water with adequate quality to users is one of the main concerns for water utilities. In most countries, this is ensured through the introduction of disinfectants, such as chlorine, which are subjected to decay over time, with consequent loss of disinfection action and the possible formation of harmful by-products. In this context, water quality models can be a useful tool to support management and, thus, ensure sufficient standards in all network points, but most of these models require the input of reaction parameters which could be difficult to obtain based on the information available to water utilities, especially in the case of complex water distribution networks (WDNs) supplied by more than one source. This study proposes a pragmatic, interval-number-based method to model chlorine decay in complex WDNs by relying on the use of the network hydraulic model and the results of trace analysis, which are exploited to obtain overall reaction rates. The method is applied to the case of a real WDN supplied by water sources with different qualitative features. The results obtained highlight that the method can help water utilities in the identification of overall water quality parameters.

This paper analyses the propagation and mechanisms of interaction of a pressure wave in a looped water distribution network by means of laboratory and numerical tests. Transients are generated by the complete and fast closure of a valve, simulating an end-user maneuver, located at the downstream end section of a service line. The adequate length of the service line allows capturing each single pressure wave inserted into the network. The executed tests and successive analysis by means of a Lagrangian model (LM) highlight the effect of the network topology and the location of the transient generation point but in a more expeditious way with respect to the use of a complete transient model. The most excited part of the system is the one in close proximity of the end-user and then the corresponding service line. Within the network, pressure waves accumulate in the areas with the smallest diameter pipes. By means of the refined LM—which is able to capture the pressure extreme values occurring in the first phases of the transient—the vulnerability maps of the network are provided. Such maps identify the nodes subjected to the most severe pressure waves in terms of both frequency and amplitude. The exposure level to transients of each node is synthesized by the value of the vulnerability index proposed in this paper.

Leakages in water distribution systems have great economic and environmental impacts and are a major issue for water utilities. In this work, the water balance and the Minimum Night Flow (MNF) method for evaluating the amount of water loss, as well as the power and Fixed and Variable Area Discharge (FAVAD) equations for analyzing the relationship between leakage and pressure, were applied to a fully monitored District Metered Area (DMA) located in Gorino Ferrarese (FE, Italy). Time series of (a) the water consumption of each user, (b) the DMA inflow, and (c) the pressure at the DMA inlet point were monitored with a 5 min time step. The results of an analysis carried out by exploiting the collected time series highlighted that: (a) The application of the MNF method based on literature values can lead to significant inaccuracies in the presence of users with irregular consumption, and (b) the estimation of the parameters of the power and FAVAD equations is highly affected by the amounts and types of observed data used.

The effectiveness of pressure-reducing valves (PRVs) for optimal pressure management of water distribution networks (WDNs) is proven, but problems and operational limitations have been highlighted by some recent experiences. In this study, we analyse the functioning of a piston-actuated pressure-reducing valve (PA-PRV) with a mechanical pilot which is subjected to low-flow regimes, a condition that is often observed in real water distribution networks. The analyses were carried out by means of laboratory tests featuring two sets of experiments, i.e., a) by testing the behaviour of the PRV when a pre-established initial value and subsequent variation of flow rate occurs in the system and b) by testing the PRV against a temporal series of flow rates observed at the inlet section of a real district metered area. The first set of tests showed that the PA-PRV tends not to maintain pressure at the imposed set-point and exhibits an unstable behaviour characterised by significant pressure oscillations under some flow rate conditions. The second set of laboratory tests showed that the anomalous behaviour identified in the first set of tests can occur under ordinary operational conditions of a network.

A real-time household water consumption monitoring and processing system aimed at leakage identification at user level is presented here. The system, developed within the GST4Water project, allows consumption data sent by a generic smart meter installed in a user’s house to be received and transferred to a cloud platform. Here, the consumption data are stored and processed through an empirical algorithm able to automatically identify leakage at the individual user level by looking for non-consumption in certain periods of the day. With reference to a real-life case study, the results obtained show that the algorithm enables leakages on users’ properties to be identified with an accuracy of more than 90%. Therefore, the implementation of this algorithm within a highly innovative smart metering system can represent an efficient tool for reducing water losses at user level.

This paper presents some of the results achieved within the framework of the GST4Water project concerning the development of a real time monitoring and processing system for water consumption at individual user level. The system is based on the most innovative technologies proposed by the ICT sector and allows for receiving consumption data sent by a generic smart-meter installed in an user’s house and transfer them to a cloud platform. Here, the consumption data are stored and processed in order to characterize leakage at district meter area (DMA) and at individual user level. Finally, the processed data, on the one hand, are returned to the Water Utility and can be used for billing, on the other hand, they provide frequent feedback to the user thus gaining full awareness of his consumption behaviour.

This document is intended to be a presentation of the Special Issue “Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems”. The final aim of this Special Issue is to propose a suitable framework supporting insightful hydraulic mechanisms to aid the decision-making processes of water utility managers and practitioners. Its 18 peer-reviewed articles present as varied topics as: water distribution system design, optimization of network performance assessment, monitoring and diagnosis of pressure pipe systems, optimal water quality management, and modelling and forecasting water demand. Overall, these articles explore new research avenues on urban hydraulics and hydroinformatics, showing to be of great value for both Academia and those water utility stakeholders.

This paper deals with the comparison of different methods which can be used for the prediction of the performance curves of pumps as turbines (PATs). The considered approaches are four, i.e., one physics-based simulation model (“white box” model), two “gray box” models, which integrate theory on turbomachines with specific data correlations, and one “black box” model. More in detail, the modeling approaches are: (1) a physics-based simulation model developed by the same authors, which includes the equations for estimating head, power, and efficiency and uses loss coefficients and specific parameters; (2) a model developed by Derakhshan and Nourbakhsh, which first predicts the best efficiency point of a PAT and then reconstructs their complete characteristic curves by means of two ad hoc equations; (3) the prediction model developed by Singh and Nestmann, which predicts the complete turbine characteristics based on pump shape and size; (4) an Evolutionary Polynomial Regression model, which represents a data-driven hybrid scheme which can be used for identifying the explicit mathematical relationship between PAT and pump curves. All approaches are applied to literature data, relying on both pump and PAT performance curves of head, power, and efficiency over the entire range of operation. The experimental data were provided by Derakhshan and Nourbakhsh for four different turbomachines, working in both pump and PAT mode with specific speed values in the range 1.53–5.82. This paper provides a quantitative assessment of the predictions made by means of the considered approaches and also analyzes consistency from a physical point of view. Advantages and drawbacks of each method are also analyzed and discussed.

This paper deals with the estimation of the energy production by means of pumps used as turbines to exploit residual hydraulic energy, as in the case of available head and flow rate in water distribution networks. To this aim, four pumps with different characteristics are investigated to estimate the producible yearly electric energy. The performance curves of Pumps As Turbines (PATs), which relate head, power, and efficiency to the volume flow rate over the entire PAT operation range, were derived by using published experimental data. The four considered water distribution networks, for which experimental data taken during one year were available, are characterized by significantly different hydraulic features (average flow rate in the range 10–116 L/s; average pressure reduction in the range 12–53 m). Therefore, energy production accounts for actual flow rate and head variability over the year. The conversion efficiency is also estimated, for both the whole water distribution network and the PAT alone.

This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods), were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.

In this article, a model for forecasting water demands over a 24-h time window using solely a pair of coefficients whose value is updated at every forecasting step is presented. The first coefficient expresses the ratio between the average water demand over the 24 h that follow the time the forecast is made and the average water demand over the 24 h that precede it. The second coefficient expresses the relationship between the average water demand in a generic hour falling within the 24-h forecasting period and the average water demand over that period. These coefficients are estimated using the information available in the weeks prior to the time of forecasting and, therefore, the model does not require any actual calibration process. The length of the time series necessary to implement the model is thus just a few weeks (3–4 weeks) and the model can be parameterized and used without there being any need to collect hourly water demand data for long periods. The application of the model to a real-life case and a comparison with results provided by another model already proposed in the scientific literature show it to be effective, robust, and easy to use.