A water distribution network (WDN) is an indispensable element of civil infrastructure that provides fresh water for domestic use, industrial development, and fire-fighting. However, in a large and complex network, operation and management (O&M) can be challenging. As a technical initiative to improve O&M efficiency, the paradigm of “divide and conquer” can divide an original WDN into multiple subnetworks. Each subnetwork is controlled by boundary pipes installed with gate valves or flow meters that control the water volume entering and leaving what are known as district metered areas (DMAs). Many approaches to creating DMAs are formulated as two-phase procedures, clustering and sectorizing, and are called water network partitioning (WNP) in general. To assess the benefits and drawbacks of DMAs in a WDN, we provide a comprehensive review of various state-of-the-art approaches, which can be broadly classified as: (1) Clustering algorithms, which focus on defining the optimal configuration of DMAs; and (2) sectorization procedures, which physically decompose the network by selecting pipes for installing flow meters or gate valves. We also provide an overview of emerging problems that need to be studied.

Operation and management of a water distribution network (WDN) by district metered areas (DMAs) bring many benefits for water utilities, particularly regarding water loss control and pressure management. However, the optimal design of DMAs in a WDN is a challenging task. This paper proposes an approach for the optimal design of DMAs in the multiple-criteria decision analysis (MCDA) framework based on the outcome of a coupled model comprising a self-organizing map (SOM) and a community structure algorithm (CSA). First, the clustering principle of the SOM algorithm is applied to construct initial homologous clusters in terms of pressure and elevation. CSA is then coupled to refine the SOM-based initial clusters for the automated creation of multiscale and dynamic DMA layouts. Finally, the criteria for quantifying the performance of each DMA layout solution are assessed in the MCDA framework. Verifying the model on a hypothetical network and an actual WDN proved that it could efficiently create homologous and dynamic DMA layouts capable of adapting to water demand variability.

A water distribution network (WDN) is a critical infrastructure that must be maintained, ensuring a proper water supply to widespread customers. A WDN consists of various components, such as pipes, valves, pumps, and tanks, and these elements interact with each other to provide adequate system performance. If the elements fail due to internal or external interruptions, this may adversely impact water service to different degrees depending on the failed elements. To determine an appropriate maintenance priority, the critical elements need to be identified and mapped in the network. To identify and prioritize the critical elements (here, we focus on the pipes only) in the WDN, an element-based simulation approach is proposed, in which all the composing pipes of the WDN are reviewed one at a time. The element-based criticality is measured using several criticality indexes that are newly proposed in this study. The proposed criticality indexes are used to quantify the impacts of element failure to water service degradation. Here, four criticality indexes are developed: supply shortage (SS), economic value loss (EVL), pressure decline (PD), and water age degradation (WAD). Each of these indexes measures different aspects of the consequences, specifically social, economic, hydraulic, and water quality, respectively. The separate values of the indexes from all pipes in a network are then combined into a singular criticality value for assessment. For demonstration, the proposed approach is applied to four real WDNs to identify and prioritize the critical pipes. The proposed element-based simulation approach can be used to identify the critical components and setup maintenance scheduling of WDNs for preparedness of failure events.

Contamination in water distribution networks (WDNs) can occur at any time and location. One protection measure in WDNs is the placement of water quality sensors (WQSs) to detect contamination and provide information for locating the potential contamination source. The placement of WQSs in WDNs must be optimally planned. Therefore, a robust sensor-placement strategy (SPS) is vital. The SPS should have clear objectives regarding what needs to be achieved by the sensor configuration. Here, the objectives of the SPS were set to cover the contamination event stages of detection, consumption, and source localization. As contamination events occur in any form of intrusion, at any location and time, the objectives had to be tested against many possible scenarios, and they needed to reach a fair value considering all scenarios. In this study, the particle swarm optimization (PSO) algorithm was selected as the optimizer. The SPS was further reinforced using a databasing method to improve its computational efficiency. The performance of the proposed method was examined by comparing it with a benchmark SPS example and applying it to DMA-sized, real WDNs. The proposed optimization approach improved the overall fitness of the configuration by 23.1% and showed a stable placement behavior with the increase in sensors.

Contamination events in water distribution networks (WDNs) could have severe health and economic consequences. Contaminants can be deliberately or accidentally introduced into the WDN. Quick identification of the injection location and time is important in devising a mitigation plan to prevent further spread of the contaminant in the network. A method of identifying the possible intrusion point in a given network and reporting data is to use an inverse calculation by backtracking the potential path of the contaminant in the network. However, there is an element of uncertainty in the data used for calculation, particularly in water flow and sensor report time. Given the uncertainties, a method was developed in this study for fast and accurate contaminant source identification. This paper proposes a comparison filter of results by first identifying potential contaminant locations through backtracking, followed by a forward calculation to determine the injection time range, thereby reducing the potential suspects and providing likeliness comparison among the suspects. The effectiveness of the proposed method was examined by applying it to a benchmark WDN. By simulating uncertainties and filtering through the results, several possible contaminant intrusion locations and times were identified.