Exploring the performance of topological approach for sensor quality placement in water distribution network

<p align="justify"><span lang="en-GB">Water distribution networks (WDNs) are an important critical infrastructure, but they are increasingly at risk from contamination (WHO, 2014). The causes can be several: chlorination equipment malfunctioning, low pressure, contaminant intrusion in water tank, accidental cross-connection between drinking-water and non-drinking-water, etc... To limit potential threat to public health, it is advisable to install a network of sensors that can monitor water quality in real time and provide information about potential contamination risks. With the proliferation of IoT technologies and low-cost sensors capable of monitoring water quality parameters, it is now possible to implement a real-time monitoring network by overcoming the difficulties associated with biochemical analyses of water samples in a laboratory. Despite the modern technologies, the placement of sensors in the water network is still an open task for researchers. The main sensor placement methodologies use optimization techniques to minimize or maximize either single- or multi-objective functions (Ostfeld et al., 2008), but they require a calibrated model of the network, which is not always available because the calibration process is expensive and time-consuming.</span></p> <p align="justify"><span lang="en-GB">Recently, a novel approach (Santonastaso et al., 2021) based on the use of the topological centrality metric, which does not require hydraulic information and simulations, has been proposed, showing good effectiveness and easy applicability by water utilities to define locations for quality sensors, owing to its simplicity compared to optimization-based approaches.</span></p> <p align="justify"><span lang="en-GB">In this work, different weights such as the length of pipes, the diameter and the water demand were used to improve the performance of the adopted topological approach, as well as to evaluate the impact of the weight, used to compute centrality metrics, in relation to the most used objective functions: number of people exposed to the contaminant; number of detected contamination events; length of contaminated pipes; amount of contaminant consumed by users; detection time of contamination.</span></p> <p align="justify">&#160;</p> <p align="justify"><span lang="en-GB">References</span></p> <p align="justify"><span lang="en-GB">World Health Organization. (&#8206;2014)&#8206;. Water safety in distribution systems. World Health Organization. </span><span lang="en-GB">https://apps.who.int/iris/handle/10665/204422</span></p> <p align="justify"><span lang="en-GB">Ostfeld A, Uber JG, Salomons E, Berry JW, Hart WE, Phillips CA, Watson JP, Dorini G, Jonkergouw P, Kapelan Z, di Pierro F, Khu ST, Savic D, Eliades D, Polycarpou M, Ghimire SR, Barkdoll BD, Gueli R, Huang JJ, McBean EA, James W, Krause A, Leskovec J, Isovitsch S, Xu J, Guestrin C, VanBriesen J, Small M, Fischbeck P, Preis A, Propato M, Piller O, Trachtman GB, Wu ZY, Walski T (2008) The battle of the water sensor networks (BWSN): a design challenge for engineers and algorithms. J Water Resour Plan Manag 134:556&#8211;568. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:6(556)</span></p> <p align="justify"><span lang="en-GB">Santonastaso, G., Di Nardo, A., Creaco, E.&#160;et al.&#160;Comparison of topological, empirical and optimization-based approaches for locating quality detection points in water distribution networks.&#160;Environ Sci Pollut Res&#160;28, 33844&#8211;33853 (2021). https://doi.org/10.1007/s11356-020-10519-3</span></p>