Version 1
: Received: 30 December 2021 / Approved: 31 December 2021 / Online: 31 December 2021 (11:01:51 CET)
How to cite:
Candelieri, A.; Ponti, A.; Archetti, F. On the Distributional Characterization of Graph Models of Water Distribution Networks in Wasserstein Spaces. Preprints2021, 2021120506. https://doi.org/10.20944/preprints202112.0506.v1
Candelieri, A.; Ponti, A.; Archetti, F. On the Distributional Characterization of Graph Models of Water Distribution Networks in Wasserstein Spaces. Preprints 2021, 2021120506. https://doi.org/10.20944/preprints202112.0506.v1
Candelieri, A.; Ponti, A.; Archetti, F. On the Distributional Characterization of Graph Models of Water Distribution Networks in Wasserstein Spaces. Preprints2021, 2021120506. https://doi.org/10.20944/preprints202112.0506.v1
APA Style
Candelieri, A., Ponti, A., & Archetti, F. (2021). On the Distributional Characterization of Graph Models of Water Distribution Networks in Wasserstein Spaces. Preprints. https://doi.org/10.20944/preprints202112.0506.v1
Chicago/Turabian Style
Candelieri, A., Andrea Ponti and Francesco Archetti. 2021 "On the Distributional Characterization of Graph Models of Water Distribution Networks in Wasserstein Spaces" Preprints. https://doi.org/10.20944/preprints202112.0506.v1
Abstract
This paper is focused on two topics very relevant in water distribution networks (WDNs): vulnerability assessment and the optimal placement of water quality sensors. The main novelty element of this paper is to represent the data of the problem, in this case all objects in a graph underlying a water distribution network, as discrete probability distributions. For vulnerability (and the related issue of re-silience) the metrics from network theory, widely studied and largely adopted in the water research community, reflect connectivity expressed as closeness centrality or, betweenness centrality based on the average values of shortest paths between all pairs of nodes. Also network efficiency and the related vulnerability measures are related to average of inverse distances. In this paper we propose a different approach based on the discrete probability distribution, for each node, of the node-to-node distances. For the optimal sensor placement, the elements to be represented as dis-crete probability distributions are sub-graphs given by the locations of water quality sensors. The objective functions, detection time and its variance as a proxy of risk, are accordingly represented as a discrete e probability distribution over contamination events. This problem is usually dealt with by EA algorithm. We’ll show that a probabilistic distance, specifically the Wasserstein (WST) distance, can naturally allow an effective formulation of genetic operators. Usually, each node is associated to a scalar real number, in the optimal sensor placement considered in the literature, average detection time, but in many applications, node labels are more naturally expressed as histograms or probability distributions: the water demand at each node is naturally seen as a histogram over the 24 hours cycle. The main aim of this paper is twofold: first to show how different problems in WDNs can take advantage of the representational flexibility inherent in WST spaces. Second how this flexibility translates into computational procedures.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
