Topological clustering for water distribution systems analysis

Perelman, Lina; Ostfeld, Avi

doi:10.1016/j.envsoft.2011.01.006

Cited by 164 publications

(85 citation statements)

References 15 publications

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“…The WDS clustering is conducted in two steps in this paper: 1) Mapping WDS into graph: the 150 WDS is mapped into an undirected graph in which the vertices represent the consumers, sources, and 151 tanks and the edges represent the connecting pipes, pumps, and valves (Perelman and Ostfeld, 2011). eliminating deficiency in the system.…”

Section: Wds Clustering 146mentioning

confidence: 99%

Optimal Rehabilitation of Water Distribution Systems Using a Cluster-Based Technique

Muhammed

Farmani

Behzadian

et al. 2017

J. Water Resour. Plann. Manage.

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Abstract:Optimal rehabilitation of large water distribution system (WDS) with many decision variables is often time-consuming and computationally expensive. This paper presents a new optimal rehabilitation methodology for WDSs based on graph theory clustering concept. The methodology starts with partitioning the WDS based on its connectivity properties into a number of clusters (small sub-systems). Pipes which might have direct impact on system performance are identified and considered for rehabilitation problem. Three optimisation-based strategies are then considered for pipe rehabilitation in the clustered network: 1) rehabilitation of some of the pipes inside the clusters; 2) rehabilitation of pipes in the path supplying water to the clusters; 3) combination of strategies 1 and 2. In all optimisation strategies, the decision variables are the diameters of duplicated pipes; the objective functions are to minimise the total cost of duplicated pipes and to minimise the number of nodes with pressure deficiency. The performance of proposed strategies was demonstrated in a large WDS with pressure deficiencies. The performance of these strategies were also compared to the full search space optimisation strategy and engineering judgement based optimisation strategy in which all pipes or selection of pipes are considered as decision variables respectively. The results show that strategy 3 is able to provide the best Pareto optimal front. The results also demonstrate that the cluster-based approach can significantly reduce the computational efforts for achieving optimum rehabilitation compared to the other optimization strategies. Optimal rehabilitation of large water distribution system (WDS) with many decision variables, 25 is often time-consuming and computationally expensive. This paper presents a new optimal 26 rehabilitation methodology for WDSs based on graph theory clustering concept. The methodology 27 starts with partitioning the WDS based on its connectivity properties into a number of clusters (small 28 sub-systems). Pipes which might have direct impact on system performance are identified and 29 considered for rehabilitation problem. Three optimisation-based strategies are then considered for 30 pipe rehabilitation in the clustered network: 1) rehabilitation of some of the pipes inside the clusters; 31 Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation2) rehabilitation of pipes in the path supplying water to the clusters; 3) combination of strategies 1 32 and 2. In all optimisation strategies, the decision variables are the diameters of duplicated pipes; the 33 objective functions are to minimise the total cost of duplicated pipes and to minimise the number of 34 nodes with pressure deficiency. 35The performance of proposed strategies was demonstrated in a large WDS with pressure 36 deficiencies. The performance of these strategies were also compared to the full search space 37 optimisation strategy and engineering judgement based optimisation strategy in which all pipes or...

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Section: Wds Clustering 146mentioning

confidence: 99%

Optimal Rehabilitation of Water Distribution Systems Using a Cluster-Based Technique

Muhammed

Farmani

Behzadian

et al. 2017

J. Water Resour. Plann. Manage.

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“…The three component types are used to augment the network graph. Perelman and Ostfeld [11] developed a multilayered clustering method as an extension of their previous clustering algorithm [12] based on depth-first search [13] and breadth-first search [14].…”

Section: Introductionmentioning

confidence: 99%

Optimal Node Grouping for Water Distribution System Demand Estimation

Jung

Choi

Kim

2016

Water

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Real-time state estimation is defined as the process of calculating the state variable of interest in real time not being directly measured. In a water distribution system (WDS), nodal demands are often considered as the state variable (i.e., unknown variable) and can be estimated using nodal pressures and pipe flow rates measured at sensors installed throughout the system. Nodes are often grouped for aggregation to decrease the number of unknowns (demands) in the WDS demand estimation problem. This study proposes an optimal node grouping model to maximize the real-time WDS demand estimation accuracy. This Kalman filter-based demand estimation method is linked with a genetic algorithm for node group optimization. The modified Austin network demand is estimated to demonstrate the proposed model. True demands and field measurements are synthetically generated using a hydraulic model of the study network. Accordingly, the optimal node groups identified by the proposed model reduce the total root-mean-square error of the estimated node group demand by 24% compared to that determined by engineering knowledge. Based on the results, more pipe flow sensors should be installed to measure small flows and to further enhance the demand estimation accuracy.

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“…The full network is decomposed into sub-networks followed by a solution of a set of sub-problems representing each of the sub-networks using evolutionary algorithms. Perelman and Ostfeld (2011) and Deuerlein (2008) used graph decomposition methods to analyze network structure and connectivity. Diao et al (2014) used network decomposition method to accelerate the hydraulic simulation process by subdividing the network into smaller subnetworks and solving the hydraulic equations of each of the sub-networks independently.…”

Section: Graphical Abstract 1 Introductionmentioning

confidence: 99%

“…The partition of the network is typically achieved by using graph algorithms, e.g. breadth first search and depth first search (Deuerlein, 2008;Perelman and Ostfeld, 2011;Ferrari et al, 2013;DiNardo et al, 2013a), multilevel partitioning (DiNardo et al, 2013b), community structure (Diao et al, 2013), and spectral approach (Her-rera et al, 2010). The selection of pipes that need to be disconnected is found by iterative procedures (Ferrari et al, 2013;Diao et al, 2013) or genetic algorithms (DiNardo et al, 2013a,b).…”

Section: Graphical Abstract 1 Introductionmentioning

confidence: 99%

Automated sub-zoning of water distribution systems

Perelman

Allen

Preis

et al. 2015

Environmental Modelling & Software

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Water distribution systems (WDS) are complex pipe networks with looped and branching topologies that often comprise of thousands to tens of thousands of links and nodes. This work presents a generic framework for improved analysis and management of WDS by partitioning the system into smaller (almost) independent sub-systems with balanced loads and minimal number of interconnection. This paper compares the performance of three classes of unsupervised learning algorithms from graph theory for practical sub-zoning of WDS: (1) Global clustering -a bottom-up algorithm for clustering n objects with respect to a similarity function, (2) Community structure -a bottom-up algorithm based on network modularity property, which is a measure of the quality of network partition to clusters versus randomly generated graph with respect to the same nodal degree, and (3) Graph partitioning -a flat partitioning algorithm for dividing a network with n nodes into k clusters, such that the total weight of edges crossing between clusters is minimized and the loads of all the clusters are balanced. The algorithms are adapted to WDS to provide a practical decision support tool for water utilities. Visual qualitative and quantitative measures are proposed to evaluate models' performance. The proposed methods are applied and results are evaluated and compared for two large-scale water distribution systems serving heavily populated areas in Singapore.

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Topological clustering for water distribution systems analysis

Cited by 164 publications

References 15 publications

Optimal Rehabilitation of Water Distribution Systems Using a Cluster-Based Technique

Optimal Rehabilitation of Water Distribution Systems Using a Cluster-Based Technique

Optimal Node Grouping for Water Distribution System Demand Estimation

Automated sub-zoning of water distribution systems

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