Consumer water demands are not typically measured at temporal or spatial scales adequate to support real‐time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real‐time modeling. This study investigates the use of a double‐seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern‐based demand models as well as nonseasonal and single‐seasonal time series models. Additional research included the implementation of an adaptive‐parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller‐scale aggregated water demands, the log‐transformed time series model resulted in improved forecasts, (2) the double‐seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real‐time modeling applications and provide a foundation for developing a real‐time integrated demand‐hydraulic model.

Drinking water distribution system models have been increasingly utilized in the development and implementation of contaminant warning systems. This study proposes a Bayesian approach for probabilistic contamination source identification using a beta-binomial conjugate pair framework to identify contaminant source locations and times and compares the performance of this algorithm to previous work based on a Bayes’ rule approach. The proposed algorithm is capable of directly assigning a probability to a potential source location and updating the probability through the use of a backtracking algorithm and Bayesian statistics. The evaluation of the performance associated with the two algorithms was conducted by a simple comparison, as well as a simulation study in terms of a conservative chemical intrusion event through both a small skeletonized network and a large all-pipe distribution system network. Results from the simple comparison showed that the beta-binomial approach was more responsive to ch...

This Editorial presents the paper collection of the Special Issue (SI) on Smart Urban Water Networks

The use of drinking water distribution system models has been around for decades and requires good demand estimates to ensure adequate hydraulic and water quality representation. Traditional demand estimation processes are capable of estimating demands, often for highly skeletonized systems, with approximations to represent uncertainties in demand estimates and hydraulic states. This study implemented a Markov chain Monte Carlo (MCMC) algorithm to estimate hourly demand multipliers and uncertainties for a synthetic network using a previously developed clustering algorithm to reduce the number of unknowns. The MCMC approach also provided the flexibility to accommodate potential spatial correlation in demand multipliers through, for example, the use of a Markov Random Field (MRF) prior. The MCMC algorithm produced adequate representation of demand multipliers, similar to weighted least squares (WLS), and improved representation of the uncertainties relative to the approximations based on WLS results. The incorporation of the MRF prior resulted in more spatially correlated demand multipliers but did not provide any significant benefits for representing the network being studied. Increasing the number of clusters, reducing measurement uncertainty, and including additional flow measurements (rather than pressure) improved the ability to represent system-wide flows. However, increasing the number of clusters also resulted in larger uncertainties in the demand multiplier estimates as the estimation problem became more ill-conditioned. A generalized discussion associated with clustering approaches and measurement locations is included to provide a broader perspective on demand estimation.

Security issues have become increasingly important within distribution systems, which have led to the development of event detection algorithms (EDAs) to provide timely detection of intrusion events. The current study develops a localized model-based event detection algorithm that utilizes nonspecific water quality sensors to identify water quality anomalies. The proposed EDA focuses on evaluating a series of multivariate error signals between the observed signals and the model estimated signals based on a moving time-window of error statistics. The likelihood of the multivariate error signals is estimated using the product of univariate kernel density estimation (KDE), which is a type of nonparametric representation of the error distribution. A comprehensive analysis was performed using synthetic events to explore the combination of the moving window-pairs and bandwidth with respect to three injection strengths and two injection durations. In addition to the synthetic events, the EDA was also eva...

Contamination events in water distribution systems (WDSs) are highly dangerous events in very vulnerable infrastructure where a quick response by water utility managers is indispensable. Various studies have explored methods to respond to water events and a variety of models have been developed to simulate the consequences and the reactions of all stakeholders involved. This study proposes a novel contamination response and recovery methodology using machine learning and knowledge of the topology and hydraulics of a water network inside of an agent-based model (ABM). An artificial neural network (ANN) is trained to predict the possible source of the contamination in the network, and the knowledge of the WDS and the possible flow directions throughout a demand pattern is utilized to verify that prediction. The utility manager agent can place mobile sensor equipment to trace the contamination spread after identifying the source to identify endangered and safe places in the water network and communicate that information to the consumer agents through water advisories. The contamination status of the network is continuously updated, and the consumers reaction and decision making are determined by a fuzzy logic system considering their social background, recent stress factors based on findings throughout the COVID-19 pandemic and their location in the network. The results indicate that the ANN-based support tool, paired with knowledge of the network, provides a promising support tool for utility managers to identify the source of a possible water event. The optimization of the ANN and the methodology led to accuracies up to 80%, depending on the number of sensors and the prediction types. Furthermore, the specified water advisories according to the mobile sensor placement provide the consumer agents with information on the contamination spread and urges them to seek for help or support less.