A proposal of optimal sampling design using a modularity strategy

Simone, Antonietta; Giustolisi, Orazio; Laucelli, Daniele Biagio

doi:10.1002/2016wr018944

Cited by 30 publications

(11 citation statements)

References 48 publications

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“…To determine the location of flow meters and gate valves, DMA design was optimized based on each conceptual cut and returned an optimal solution for each one, accounting for hydraulic behavior change in the network with respect to maximizing the reduction of background leakage in each DMA. Using the WDN-oriented modularity index, Simone et al [68] developed a sampling-oriented modularity index to perform optimal spatial distribution and assess the optimal number of pressure meters needed in a network (i.e., sampling design) using a multi-objective optimization method to minimize pressure-meter cost versus sampling-oriented modularity.…”

Section: Modularity-based Algorithmmentioning

confidence: 99%

Water Network Partitioning into District Metered Areas: A State-Of-The-Art Review

Bui

Marlim

Kang

2020

Water

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A water distribution network (WDN) is an indispensable element of civil infrastructure that provides fresh water for domestic use, industrial development, and fire-fighting. However, in a large and complex network, operation and management (O&M) can be challenging. As a technical initiative to improve O&M efficiency, the paradigm of “divide and conquer” can divide an original WDN into multiple subnetworks. Each subnetwork is controlled by boundary pipes installed with gate valves or flow meters that control the water volume entering and leaving what are known as district metered areas (DMAs). Many approaches to creating DMAs are formulated as two-phase procedures, clustering and sectorizing, and are called water network partitioning (WNP) in general. To assess the benefits and drawbacks of DMAs in a WDN, we provide a comprehensive review of various state-of-the-art approaches, which can be broadly classified as: (1) Clustering algorithms, which focus on defining the optimal configuration of DMAs; and (2) sectorization procedures, which physically decompose the network by selecting pipes for installing flow meters or gate valves. We also provide an overview of emerging problems that need to be studied.

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Section: Modularity-based Algorithmmentioning

confidence: 99%

Water Network Partitioning into District Metered Areas: A State-Of-The-Art Review

Bui

Marlim

Kang

2020

Water

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“…The edge closeness is a measure of the edge efficiency in spreading the information, and it consists in considering the distance between pipes rather than between nodes. To this aim, one can refer to the line graph or the edge adjacency matrix, which represents the adjacencies between edges of the network (Simone et al, ). Therefore, the formulation is

C_{l}^{C} = \frac{1}{\sum_{e} d_{l, e}}

where C l C is the edge closeness of the edge l and ∑ d l,e is the sum of the steps (path) from edge l to edges e of the network.…”

Section: Tailoring Centrality Metrics For Wdns: Node Versus Edge Cenmentioning

confidence: 99%

Tailoring Centrality Metrics for Water Distribution Networks

Giustolisi

Ridolfi

Simone

2019

Water Resources Research

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Complex network theory (CNT) is an emerging topic based on the paradigm that quite all the natural and man‐made physical systems work as networks, namely, their features derive from the internal connectivity among vertices exchanging information through edges. Water distribution infrastructures are networked systems connecting the vertices named nodes, by edges named pipes, and transferring water to customers. Therefore, water distribution networks (WDNs) fall into CNT and belongs to the class of spatial networks due to their urban constraints. CNT proposed several centrality metrics for quantifying the importance of vertex and, sometime, edges. Those metrics can be potentially very useful for analyzing the key features of the physical domain (i.e., the network) where WDN hydraulics occurs, but they need to be tailored to consider that (i) pipes/edges are the relevant physical components of the WDNs, (ii) some nodes/vertices (reservoirs and tanks) play a completely different hydraulic role from the majority of nodes (demand and connection nodes), and (iii) pipes/edges have different characteristics (length, diameter, hydraulic resistance, etc.). Accordingly, this work presents and discusses the need of tailoring the most suitable centrality metrics for spatial networks: betweenness, closeness, and degree. Then the capacity of the WDN‐tailored edge betweenness is demonstrated and discussed using two real WDNs, showing that it can extract useful information from the domain, that is, the emerging hydraulic behavior due to the network connectivity structure. Therefore, the WDN‐tailored edge betweenness can assist analysis, planning, and management actions before and after the hydraulic analysis.

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“…Although a few studies have identified a tradeoff relationship between the meter cost and detection effectiveness measure-mostly by using a constrained single-objective model [9,10,17]-DP-type measures (e.g., MeterRob or DP) and RF have not previously been optimized simultaneously. The non-dominated sorting genetic algorithm-II (NSGA-II) [18] was used to explore the tradeoff relationship between the objectives of the MOMP model.…”

Section: Multi-objective Optimal Meter Placement (Momp) Modelmentioning

confidence: 99%

Robust Meter Network for Water Distribution Pipe Burst Detection

Jung

Kim

2017

Water

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Abstract:A meter network is a set of meters installed throughout a water distribution system to measure system variables, such as the pipe flow rate and pressure. In the current hyper-connected world, meter networks are being exposed to meter failure conditions, such as malfunction of the meter's physical system and communication system failure. Therefore, a meter network's robustness should be secured for reliable provision of informative meter data. This paper introduces a multi-objective optimal meter placement model that maximizes the detection probability, minimizes false alarms, and maximizes the robustness of a meter network given a predefined number of meters. A meter network's robustness is defined as its ability to consistently provide quality data in the event of meter failure. Based on a single-meter failure simulation, a robustness indicator for the meter network is introduced and maximized as the third objective of the proposed model. The proposed model was applied to the Austin network to determine the independent placement of pipe flow and pressure meters with three or five available meters. The results showed that the proposed model is a useful tool for determining meter locations to secure high detectability and robustness.

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A proposal of optimal sampling design using a modularity strategy

Cited by 30 publications

References 48 publications

Water Network Partitioning into District Metered Areas: A State-Of-The-Art Review

Water Network Partitioning into District Metered Areas: A State-Of-The-Art Review

Tailoring Centrality Metrics for Water Distribution Networks

Robust Meter Network for Water Distribution Pipe Burst Detection

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