Emerging Issues and Methodologies for Resilient and Robust Water Distribution Systems

Jung, Donghwi; Kim, Joong Hoon

doi:10.3390/w12030769

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…More recently [22] proposed a metric based on robustness and redundancy to evaluate resilience along with an optimization framework. A basic recent reference is [23].…”

Section: Related Workmentioning

confidence: 99%

A Novel Graph-Based Vulnerability Metric in Urban Network Infrastructures: The Case of Water Distribution Networks

Ponti

Candelieri

Giordani

et al. 2021

Water

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The key contribution of this paper is to embed the analysis of the network in a framework based on a mapping from the input space whose elements are nodes of a graph or the entire graph into an information space whose elements are probability distributions associated to objects in the input space. Specifically, a node is associated to the probability distribution of its node-to-node distances and the whole graph to the aggregation of these node distributions. In this space two distances are proposed for this analysis: Jensen-Shannon and Wasserstein, based respectively on information theory and optimal transport theory. This representation allows to compute the distance between the original network and the one obtained by the removal of nodes or edges and use this distance as an index of the increase in vulnerability induced by the removal. In this way a new characterization of vulnerability is obtained. This new index has been tested in two real-world water distribution networks. The results obtained are discussed along those which relate vulnerability to the loss of efficiency and those given by the analysis of the spectra of the adjacency and Laplacian matrices of the network. The models and algorithms considered in this paper have been integrated into an analytics framework which can also support the analysis of other networked infrastructures among which power grids, gas distribution, and transit networks are included.

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“…More recently [22] proposed a metric based on robustness and redundancy to evaluate resilience along with an optimization framework. A basic recent reference is [23].…”

Section: Related Workmentioning

confidence: 99%

A Novel Graph-Based Vulnerability Metric in Urban Network Infrastructures: The Case of Water Distribution Networks

Ponti

Candelieri

Giordani

et al. 2021

Water

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“…Approaches base on graph theory have been the subject of several papers [7,8]. Other approaches have been focused on the general issue of resilience [9][10][11]. Giustolisi et al [12] proposes a new statistical distribution of the node degree.…”

Section: Related Workmentioning

confidence: 99%

On the Distributional Characterization of Graph Models of Water Distribution Networks in Wasserstein Spaces

Candelieri¹,

Ponti²,

Archetti³

2021

Preprint

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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.

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Emerging Issues and Methodologies for Resilient and Robust Water Distribution Systems

Cited by 2 publications

References 20 publications

A Novel Graph-Based Vulnerability Metric in Urban Network Infrastructures: The Case of Water Distribution Networks

A Novel Graph-Based Vulnerability Metric in Urban Network Infrastructures: The Case of Water Distribution Networks

On the Distributional Characterization of Graph Models of Water Distribution Networks in Wasserstein Spaces

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