Social Network Community Detection and Hybrid Optimization for Dividing Water Supply into District Metered Areas

Brentan, Bruno Melo; Campbell, Enrique; Goulart, Thaisa Dias; Manzi, Daniel; Lima, Gustavo Meirelles; Herrera, Manuel; Izquierdo, Joaquín; Luvizotto, Edevar

doi:10.1061/(asce)wr.1943-5452.0000924

Cited by 28 publications

(23 citation statements)

References 27 publications

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“…Sometimes, prior expert knowledge can be introduced in the form of rules (Gibert et al, 2010b) or ontologies (Gibert et al, 2014) to introduce semantic information into the process, and get classes easier to interpret. Graph theory (Herrera et al, 2015;di Nardo et al, 2018) and social network theory (Campbell et al, 2016;Brentan et al, 2017aBrentan et al, , 2018a have also found applications in clustering. Density-based methods, like DBScan (Ester et al, 1996) or OPTICS (Ankerst et al, 1999) are computation-based methods detecting areas with higher concentration of objects and work well with non-globular clusters.…”

Section: Profiling Dm Methods: Clustering and Density Estimationmentioning

confidence: 99%

“…A hybrid combination SOM+k-Means Clustering was used to improve planning, operation and management of Water Distribution Systems in Brentan et al (2018b), which can be easily extended to other environmental problems, etc. Graph theory (Herrera et al, 2015;di Nardo et al, 2018) and social network theory (Campbell et al, 2016;Brentan et al, 2017aBrentan et al, ,2018a have been used to cluster a water distribution network into sectors so as to optimize these infrastructures' management. In Blanco et al (2018) SCADA data is used to identify health profiles of wind turbines.…”

Section: Applications and Referencesmentioning

confidence: 99%

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Which method to use? An assessment of data mining methods in Environmental Data Science

Gibert

Izquierdo

Sànchez–Marrè

et al. 2018

Environmental Modelling & Software

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Data Mining (DM) is a fundamental component of the Data Science process. Over recent years a huge library of DM algorithms has been developed to tackle a variety of problems in fields such as medical imaging and traffic analysis. Many DM techniques are far more flexible than more classical numerial simulation or statistical modelling approaches. These could be usefully applied to data-rich environmental problems. Certain techniques such as artificial neural networks, clustering, case-based reasoning or Bayesian networks have been applied in environmental modelling, while other methods, like support vector machines among others, have yet to be taken up on a wide scale. There is greater scope for many lesser known techniques to be applied in environmental research, with the potential to contribute to addressing some of the current open environmental challenges. However, selecting the best DM technique for a given environmental problem is not a simple decision, and there is a lack of guidelines and criteria that helps the data scientist and environmental scientists to ensure effective knowledge extraction from data. This paper provides a broad introduction to the use of DM in Data Science processes for environmental researchers. Data Science contains three main steps (pre-processing, data mining and post-processing). This paper provides a conceptualization of Environmental Systems and a conceptualization of DM methods, which are in the core step of the Data Science process. These two elements define a conceptual framework that is on the basis of a new methodology proposed for relating the characteristics of a given environmental problem with a family of Data Mining methods. The paper provides a general overview and guidelines of DM techniques to a non-expert user, who can decide with this support which is the more suitable technique to solve their problem at hand. The decision is related to the bidimensional relationship between the type of environmental system and the type of DM method. An illustrative two way table containing references for each pair Environmental System-Data Mining method is presented and discussed. Some examples of how the proposed methodology is used to support DM method selection are also presented, and challenges and future trends are identified.

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Section: Profiling Dm Methods: Clustering and Density Estimationmentioning

confidence: 99%

Section: Applications and Referencesmentioning

confidence: 99%

Which method to use? An assessment of data mining methods in Environmental Data Science

Gibert

Izquierdo

Sànchez–Marrè

et al. 2018

Environmental Modelling & Software

Self Cite

View full text Add to dashboard Cite

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“…Evaluation of DMAs scenarios after sectorization must also guarantee that hydraulic indicators are at an acceptable or higher threshold compared with the original network. Because different criteria lead to various DMA layouts, Brentan et al [43,44] proposed a method that considers the relationship between many technical criteria, such as demand and pipe length, to create different DMA scenarios. The social community detection algorithm was used to define DMAs.…”

Section: Modularity-based Algorithmmentioning

confidence: 99%

“…The goal is to determine the optimal number of DMAs to balance the number of nodes in each cluster and to minimize the number of boundary pipes (i.e., pipe cuts where gate valves or flow meters will be installed). The algorithms applied include graph theory such as depth-first search (DFS) and breadth-first search (BFS) [6,9,38,39], community structure [19,34,37,40], modularity-based procedures [41][42][43][44], multilevel partitioning [17,37,45,46], spectral approaches [47][48][49], and multi-agent approaches [50][51][52]. This paper focuses on explaining six major algorithms and how they are handled in clustering WDNs to automatically create DMA configurations.…”

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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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“…• Community structure [42]: This structure happens when subsets of nodes within node-node connections are dense but between which are less dense. Communities in a social network straightforwardly extend to applications in biology [43], ecology [44], engineering [45], and industry [46], among others. The property of modularity [47] is often used for detecting community structures.…”

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confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

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Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.Processes 2020, 8, 312 2 of 29 in a telecommunication system network, nodes (e.g., routers and switches) may suffer over time a degradation in their properties that may lead them to diminish their performance. However, just the daily variation on traffic demand also has an impact on such nodes performance. Unexpected, external intervention such as cyber-attacks or extreme weather conditions may affect the properties, even the topology of a telecommunication system.Intelligent distributed systems are capable of modelling how different parts of the network might work individually and collectively [10]. Ultimately, this is used as support for any decision-making system aiming to achieve better overall system functioning and its consequent performance. Intelligent distributed systems encompass autonomous learning units that can be associated with the nodes of a complex network. These intelligent network nodes are able to act independently and also interact with other nodes to pursue both individual (local) and general system-level targets. The necessary communication between individuals can be represented by links of the complex network connecting their nodes. It is natural to understand such nodes as intelligent agents within a MAS framework. Each agent separately obeying simple rules but working together with other agents makes it possible to approach complex engineering challenges. Intelligent distributed systems have been proposed in a broad range of engineered complex systems [11,12]. Precisely, the review proposed herein focuses only on engineered systems and manufacturing processes, given the wide spectrum of the approached topic. Out of this review fall, then, topics related to social systems analysis such as migration models [13,14] or online social media [15,16]. Similarly, natural systems are out of this review scope. This is the case of computational chemistry [17,18], biology [19,20], and metabolic networks [21,22].Automatic optimisation and control in an engineering system is associated with a near real-time data acquisition and an optimal decision-making. Th...

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Social Network Community Detection and Hybrid Optimization for Dividing Water Supply into District Metered Areas

Cited by 28 publications

References 27 publications

Which method to use? An assessment of data mining methods in Environmental Data Science

Which method to use? An assessment of data mining methods in Environmental Data Science

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

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

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