Emerging trends of resilient and reliable water infrastructure advocate for the development of efficient state estimation (SE) techniques in water distribution systems (WDSs). SE refers to estimating the flows and heads in the WDS at unmonitored locations based on measurements collected from limited monitoring locations. Current physics-based SE methods typically require more exhaustive than readily available information about the WDS and are computationally demanding to attain real-time SE fully. Using neural networks for SE is a promising avenue because neural networks are more adaptable to the availability of sensory data and can shift most of the computation efforts to the offline training phase. Once trained, the inference is more computationally efficient compared to the physics-based SE methods. This work proposes a graph neural network (GNN) model for SE in WDSs. Unlike traditional neural networks, GNNs are more suitable for the SE problem for two main reasons: (1) given a limited number of monitoring locations, the SE problem inherently requires a semisupervised learning method, and (2) GNNs enable learning from the graph structure of a WDS, thus providing a mechanism to incorporate the functional relationships between the monitored and unmonitored locations and incorporate the physical laws during the training process. To evaluate the performance of GNNs for SE, we tested supervised and semisupervised approaches, investigated the impact of GNN architecture choices on its performance, and examined model performance under different levels of noise in the training data. The results demonstrate that GNNs are promising for SE for their ability to learn from graph structure with a limited amount of information while exhibiting robustness to noise. This study contributes toward advancing real-time GNN-based SE in WDSs. Future research is needed to incorporate various hydraulic devices and investigate the scalability of GNNs to large-scale WDSs.

Water distribution systems are vulnerable to hazards that threaten water delivery, water quality, and physical and cybernetic infrastructure. Water utilities and managers are responsible for assessing and preparing for these hazards, and researchers have developed a range of computational frameworks to explore and identify strategies for what-if scenarios. This manuscript conducts a review of the literature to report on the state of the art in modeling methodologies that have been developed to support the security of water distribution systems. First, the major activities outlined in the emergency management framework are reviewed; the activities include risk assessment, mitigation, emergency preparedness, response, and recovery. Simulation approaches and prototype software tools are reviewed that have been developed by government agencies and researchers for assessing and mitigating four threat modes, including contamination events, physical destruction, interconnected infrastructure cascading failures, and cybernetic attacks. Modeling tools are mapped to emergency management activities, and an analysis of the research is conducted to group studies based on methodologies that are used and developed to support emergency management activities. Recommendations are made for research needs that will contribute to the enhancement of the security of water distribution systems.

Social distancing policies (SDPs) implemented throughout the United States in response to COVID-19 have led to spatial and temporal shifts in drinking water demand and, for water utilities, created sociotechnical challenges. During this unique period, many water utilities have been forced to operate outside of design conditions with reduced workforce and financial capacities. Few studies have examined how water utilities respond to a pandemic; such methods are even absent from many emergency response plans. Here, we documented how utilities have been impacted by the COVID-19 pandemic. We conducted a qualitative analysis of 30 interviews with 53 practitioners spanning 28 U.S. water utilities. Our aim was to, first, understand the challenges experienced by utilities and changes to operations (e.g., demand and deficit accounts) and, second, to document utilities’ responses. Results showed that to maintain service continuity and implement SDPs, utilities had to overcome various challenges. These include supply chain issues, spatiotemporal changes in demand, and financial losses, and these challenges were largely dependent on the type of customers served (e.g., commercial or residential). Examples of utilities’ responses include proactively ordering extra supplies and postponing capital projects. Although utilities’ adaptations ensured the immediate provision of water services, their responses might have negative repercussions in the future (e.g., delayed projects contributing to aging infrastructure).

This paper presents a dataset with two hundred and eighty sensory measurements for leak detection and localization in water distribution systems. The data were generated via a laboratory-scale water distribution system that included (1) three types of sensors: accelerometer, hydrophone, and dynamic pressure sensor; (2) four leak types: orifice leak, longitudinal and circumferential cracks, gasket leak, and no-leak condition; (3) two network topologies: looped and branched; and (4) six background conditions with different noise and demand variations. Each measurement was 30 s long, and the measurement frequencies were 51.2 kHz for the accelerometer and dynamic pressure sensors, and 8 kHz for the hydrophone. This is the first publicly available dataset for advancing leak detection and localization research, model validation, and generating new data for faulty sensor detection in water distribution systems.