Battle of the Water Networks District Metered Areas

Saldarriaga, Juan; Bohórquez, Jessica; Celeita, David; Luis, Fernandez Vega,; Páez, D.; Savić, Dragan; Dandy, Graeme C.; Filion, Y. R.; Grayman, Walter M.; Kapelan, Zoran

doi:10.1061/(asce)wr.1943-5452.0001035

Cited by 35 publications

(23 citation statements)

References 18 publications

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“…The total cost represents the cost involved in the installation of PRVs, while the unit cost represents the cost per valve implanted. The cost for PRV are based on Saldarriaga et al (2019). This analysis was made in order to obtain insights on the costs associated with the pressure optimization.…”

Section: Optimization Of Entrance Location and Operational Point Of Vrpsmentioning

confidence: 99%

Optimal pressure management in water distribution networks through district metered area creation based on machine learning

Novarini

Brentan

Lima

et al. 2019

RBRH

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Integrated management of water supply systems with efficient use of natural resources requires optimization of operational performances. Dividing the water supply networks into small units, so-called district metered areas (DMAs), is a strategy that allows the development of specific operational rules, responsible for improving the network performance. In this context, clustering methods congregate neighboring nodes in groups according to similar features, such as elevation or distance to the water source. Taking into account hydraulic, operational and mathematical criteria to determine the configuration of DMAs, this work presents the k-means model and a hybrid model, that combines a self-organizing map (SOM) with the k-means algorithm, as clustering methods, comparing four mathematical criteria to determine the number of DMAs, namely Silhouette, GAP, Calinski-Harabasz and Davies Bouldin. The influence of three clustering topological criteria is evaluated: the water demand, node elevation and pipe length, in order to determine the optimal number of clusters. Furthermore, to identify the best DMA configuration, the particle swarm optimization (PSO) method was applied to determine the number, cost, pressure setting of Pressure Reducing Valves and location of DMA entrances.

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Section: Optimization Of Entrance Location and Operational Point Of Vrpsmentioning

confidence: 99%

Optimal pressure management in water distribution networks through district metered area creation based on machine learning

Novarini

Brentan

Lima

et al. 2019

RBRH

View full text Add to dashboard Cite

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“…Most researchers agree that partitioning a network into DMAs offers many benefits [2][3][4][5][6]. These actions may include but are not limited to: (i) Substantially reduce nonrevenue water by active leakage control; (ii) simplify pressure management by setting off pressure reducing valves (PRVs); (iii) rapidly identify burst pipes; (iv) district isolation in order to protect the rest of network from accidental or malicious contamination events; and (v) potential creation of independent DMAs which exclusively supplied from its own water sources for better control of water quality (e.g., there is no mixing of water from different sources).…”

Section: Introductionmentioning

confidence: 99%

“…The clustering phase is the preformation of DMAs based on network connectivity and topology. It is implemented through various algorithms such as graph theory, community structure, modularity-based algorithms, and spectral algorithms [2,3] to form feasible DMAs and minimize the number of connections to each other. Sectorization is an optimization process to locate flow meters and gate valves to maintain as high as possible network performances while minimizing the economic costs [6].…”

Section: Introductionmentioning

confidence: 99%

“…In the 18th Water Distribution System Analysis Conference held in Colombia in July 2016, the "Battle of Water Networks" competition focused on creating DMAs. The main objective was to optimize the design and operation of a system's main components by determining new DMAs for an existing WDN in Colombia, the E-Town network, by taking into account costs, pressure uniformity, and water quality [3]. It was an opportunity for researchers and practitioners around the world to solve a challenging problem in a full-scale WDN.…”

Section: Introductionmentioning

confidence: 99%

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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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“…In order to facilitate monitoring and control of water distribution networks (WDNs), these are often divided into subnetworks called district metered areas (DMAs) [6]. This sectioning in small systems with at most 3 000 nodes facilitates the isolation and control of leaks [7].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Node Pressures in Water Distribution Networks by Gaussian Process Regression

Santos‐Ruiz

López‐Estrada

Puig

et al. 2019

2019 4th Conference on Control and Fault Tolerant Systems (SysTol)

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This paper proposes a method to predict pressures in all nodes of a water distribution network (WDN) by Gaussian process regression (GPR) from pressure measurements in a subset of selected nodes. The pressure sensors are placed in the nodes where, together, they capture the maximum pressure variance and also have a minimum sensitivity to measurement noise. As a case study, the proposed method was tested on a dataset obtained from simulations with the hydraulic model of the Hanoi WDN. Using only three pressure sensors, the GPR estimation error in the pressures of the unmeasured nodes are comparable to the error due to measurement noise in physical pressure sensors.

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Battle of the Water Networks District Metered Areas

Cited by 35 publications

References 18 publications

Optimal pressure management in water distribution networks through district metered area creation based on machine learning

Optimal pressure management in water distribution networks through district metered area creation based on machine learning

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

Estimation of Node Pressures in Water Distribution Networks by Gaussian Process Regression

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