Sectorization of a water distribution network (WDN) into district meter areas (DMAs) is considered a key strategy for an efficient management of WDNs. Typically, it involves a two-stage procedure: a clustering stage, in which the division of the WDN into clusters is performed, and a dividing stage, which involves the placement of valves and flow meters on the cluster’s boundary pipes to define the DMAs. While recently published methods attempt to enhance both the clustering and dividing stages, they fail to provide decision-making flexibility. They also neglect to consider the presence of existing valves in the WDN, which can significantly affect the evaluated implementation cost, often considered the primary decision-driving factor. This paper presents improvements to the previously introduced DeNSE method for sectorization of WDNs, aiming to address these deficiencies. The methodology consists of a clustering stage, based on the network uniformity index, and a dividing stage, in which the originally used heuristic procedure is replaced with Genetic Algorithm (GA) optimization, minimizing implementation cost. Consideration of existing valves in WDN and criteria for water supply security are also included in the dividing stage to offer a better estimate of implementation costs and post-sectorized operational efficiency of the WDN. Finally, GIS visualization is implemented, and a hydraulic model of the sectorized WDN (EPANET file) is generated, providing practitioners with valuable insights and decision-making flexibility. The methodology is tested on a part of the Amsterdam WDN in the Netherlands, serving as a pilot for methodology evaluation. A range of feasible sectorization solutions is generated and compared based on implementation cost and three performance indicators (PIs). The paper provides an in-depth discussion on the selection of preferable sectorization solution. The reported results demonstrate the method’s efficiency in optimizing sectorization solutions with minimum implementation cost whilst preserving the WDN operational efficiency and meeting the local design criteria.

@en

The knowledge of water levels and discharges in urban drainage and stormwater management (UDSM) systems is of key importance to understand their functioning and processes, to evaluate their performance, and to provide data for modelling. In this chapter, devoted mainly to underground combined and separate sewer pipe systems, various methods and technologies are described and discussed. After an introduction to important aspects to deal with when measuring discharges in sewer systems, the following parts are presented successively: (i) measurement of water level with rulers, and pressure, ultrasonic and radar sensors, (ii) measurement of flow velocity with ultrasonic, Doppler, velocity profiler, free surface, and electromagnetic sensors, (iii) direct measurement of discharge with pre-calibrated devices, physical scale models, computational fluid dynamics modelling and use of pumping stations, and (iv) detection and/or measurement of infiltration into and exfiltration from sewers, with flow or pressure measurements, tracer experiments, distributed temperature sensing and geophysical methods.@en

Different methods have been developed in the past to formulate and solve steady-state hydraulics of a water distribution system (WDS). The most widely used method nowadays is probably the global gradient algorithm (GGA). The loop-flow method (also known as the ΔQ method) represents a viable alternative to GGA, especially when combined with suitably preprocessed network data. The main advantage of the ΔQ method over the GGA is in the smaller number of unknowns to solve for, which is coming from the fact that real WDSs typically have far less loops than nodes. A new loop-flow-type method, relying on the novel triangulation based loops identification algorithm (TRIBAL) that was implemented in the corresponding new hydraulic solver (ΔQ), is presented in this paper (TRIBAL-ΔQ). The new method aims to exploit this advantage, while overcoming key drawbacks of existing ΔQ methods. The performance of the TRIBAL- ΔQ-based solver is compared with the GGA-based solver on four large real networks of different complexity and topology. The results obtained demonstrate that, despite requiring an increased number of iterations to converge, the TRIBAL-ΔQ method-based solver is substantially computationally faster, has slightly better numerical stability, and is equally accurate in making predictions when compared with the GGA-based hydraulic solver.

@en