However, the success of optimal sensor-placement in case of old water networks with uncertainties in their numerical models is uncertain on its own. These studies focus on how to deploy sensors (or how to use them economically, if there are many) from which the method of reconstruction follows. The time-dependent signal is reconstructed perfectly by observing 30 % of the nodes. Introduced a novel graph Fourier-transform methodology to define sensor placement for observing transient phenomena, namely pollutant spread. The method achieved a minimal average mutual coherence of around 50 % on the models of real world water networks in steady-state scenarios and with only a handful of observed nodes. ( 2017) developed an algorithm to optimally distribute the sensors over WDSs by minimizing the mutual coherence and focusing on leak detection. Graph neural networks by following the considerations discussed in the paper. Model reconstructs the nodal pressure with at most 5 The weightedĬonnections prove no benefit over the binary connections, but the proposed Number of nodes observed compared to the total number of nodes. The performance of the proposed model is presented on 3 WDSs at different Loss can be embed into the spectral graph filters through the adjacency matrix. In addition, a weighting method is shown, wherewith information on friction Kernel is discussed taking into account the peculiarities of the application. Layers, layer depth and the degree of the Chebyshev-polynomial applied in the Graph convolution on water networks is possible. Reconstruction method is based on K-localized spectral graph filters, wherewith Observing only a limited number of nodes is presented in the paper. The data-driven methodology of reconstructing all the nodal pressures by Yet, complete measurement dataĬannot be collected due to the limited number of instruments in a real-life (WDS) facilitates safe and efficient operation. Knowing the pressure at all times in each node of a water distribution system
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