Multi-criteria pressure sensors placement in water distribution networks using fuzzy TOPSIS

Abstract

Pressure data collection is essential to increase insight into the current condition of water distribution networks (WDNs). To this end, several methods have been proposed over the last decades for measurement site design (MSD). This research presents a novel method for designing measurement sites by using the k-means clustering algorithm as a pre-processing step, followed by utilizing a new optimization algorithm coupled with the fuzzy TOPSIS method as a processing step. The k-means clustering algorithm is employed to narrow down the search space and identify the most suitable candidate nodes. These candidate nodes are then fed into the new optimization algorithm, called the binary genetic-differential evolutionary algorithm (BGDE), to find the optimal nodes, which are then sorted using the fuzzy TOPSIS method. The BGDE considers sensitivity and entropy as objective functions, while the investment cost is taken into account as a constraint. Furthermore, the Bayesian model averaging (BMA) is employed to mitigate the uncertainties in pipe roughness and nodal demands in the hydraulic simulation model. To evaluate the efficiency of the novel method, two WDNs are tested— one from the literature and the other from a real-world case study. Results show that the proposed method reduces the search space, leading to a 70% faster execution, although the accuracy in finding optimal nodes is reduced by roughly 15% compared to the benchmark method.