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Blockage, which arises from accumulated materials like sand, stones, sediment, uncontrolled growth of tree roots within pipes, and the inadvertent partial closure of in-line valves, poses a pervasive challenge in the operational sphere of water distribution networks (WDNs). This results in significant head loss in the pipes, leading to disruptions in the water supply. Identifying such blockages conventionally requires specialized human resources and is often encumbered by cost and time challenges. This paper introduces a method for precisely determining the location and magnitude of blockages within WDNs. This methodology is firmly grounded in a calibration-optimization framework, harnessing a specially crafted hybrid optimization algorithm known as PSOHS, which combines the strengths of particle swarm optimization (PSO) and harmony search (HS) algorithms. Detailed comparisons are drawn between simulated pressure data (derived from the hydraulic network model) and corresponding field data at multiple points to minimize disparities, thereby facilitating the precise identification of potential blockages in a WDN. The proposed method undergoes rigorous evaluation across two benchmark networks (modified Poulakis and Hanoi) under diverse scenarios as well as on an actual WDN. Results from over 60,000 simulated blockage scenarios underscore the method's remarkable proficiency, achieving blockage localization accuracy of 99.9% and magnitude estimation with a maximum absolute error (MAE) of less than 3%. Particularly noteworthy is the superior performance of the PSOHS algorithm as proposed, surpassing the PSO algorithm by enhancing blockage localization accuracy by a minimum of 10.5% and reducing MAE by 3%. Further, adopting the proposed algorithm over the HS algorithm yields improvements of 27.5% and 3.5%, respectively. Applying the proposed method in an actual network effectively pinpointed its blockage. Consequently, this algorithm presents a valuable tool for WDN operators, augmenting their capacity for effective network management. [ABSTRACT FROM AUTHOR]

This paper presents a two-dimensional (2D) shallow-water numerical model, which is based on the resolution of the Saint–Venant equation using the unstructured finite-volumes method, combined with Green’s theorem technique. The model has been validated by several benchmarks. The numerical results obtained from the model are in good agreement with the analytical or experimental ones. The paper also presents an application of this model to flood diversion from the Red River into a water-retention zone for the purpose of reducing flood threat at Hanoi, capital of Vietnam. [ABSTRACT FROM AUTHOR]

Design of water distribution networks (WDNs) is classified as a large combinatorial discrete nonlinear nondeterministic polynomial-hard (NP-hard) optimization problem. The main concerns associated with the optimization of WDNs are related to the nonlinearity of the discharge–head loss relationships for pipes and the discrete nature of pipe sizes. This paper proposes a new evolutionary algorithm, called fittest individual referenced differential evolution (FDE), which is significantly more efficient and reliable than other algorithms for optimization of WDN designs. The fundamental structure of FDE is similar to conventional differential evolution (DE), although the functions of the mutation and crossover operators are to exploit good solutions, and explore very different solutions, respectively, and counting and restarting mechanisms are introduced to identify and avoid local optima. These features are beneficial in WDN design because such problems may have an array of local optima that are far from one another, making finding the global optima difficult. The mutation and crossover operators are developed to accelerate convergence and, although the optimal mutation and crossover parameters may vary among networks, the efficiency of the algorithm reduces the need for adaptive parameters or population size. A sensitivity analysis is conducted for algorithm parameters, as well as counting and restarting limits, based on eight evolutionary algorithm (EA) benchmark test problems, and the efficiency of the FDE for solving 2- and 10-dimensional versions of these problems is demonstrated using the most effective values of the parameters and limits. FDE-based optimal designs of four benchmark networks—the Two-loop, Hanoi, and New York WDNs and the Balerma Irrigation Network—show that on average, a minimal number of function evaluations (or hydraulic simulations) is required to reach the best known optimum, which is less than or equal to 600 in all cases for the first three networks. Furthermore, a detailed application of FDE to a large-sized network, that for the City of Farhadgerd, Iran, shows that FDE is significantly more effective than other EAs in terms of its speed of convergence and reliability. [ABSTRACT FROM AUTHOR]