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Using Complex Network Analysis for Optimization of Water Distribution Networks

The optimization of water networks supports the decision‐making process by identifying the optimal trade‐off between costs and performance (e.g., resilience and leakage). A major challenge in the domain of water distribution systems (WDSs) is the network (re)design. While the complex nature of WDS h...

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Detalles Bibliográficos
Autores principales: Sitzenfrei, Robert, Wang, Qi, Kapelan, Zoran, Savić, Dragan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507211/
https://www.ncbi.nlm.nih.gov/pubmed/32999510
http://dx.doi.org/10.1029/2020WR027929
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author Sitzenfrei, Robert
Wang, Qi
Kapelan, Zoran
Savić, Dragan
author_facet Sitzenfrei, Robert
Wang, Qi
Kapelan, Zoran
Savić, Dragan
author_sort Sitzenfrei, Robert
collection PubMed
description The optimization of water networks supports the decision‐making process by identifying the optimal trade‐off between costs and performance (e.g., resilience and leakage). A major challenge in the domain of water distribution systems (WDSs) is the network (re)design. While the complex nature of WDS has already been explored with complex network analysis (CNA), literature is still lacking a CNA of optimal water networks. Based on a systematic CNA of Pareto‐optimal solutions of different WDSs, several graph characteristics are identified, and a newly developed CNA design approach for WDSs is proposed. The results show that obtained designs are comparable with results found by evolutionary optimization, but the CNA approach is applicable for large networks (e.g., 150,000 pipes) with a substantially reduced computational effort (runtime reduction up to 5 orders of magnitude).
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spelling pubmed-75072112020-09-28 Using Complex Network Analysis for Optimization of Water Distribution Networks Sitzenfrei, Robert Wang, Qi Kapelan, Zoran Savić, Dragan Water Resour Res Research Articles The optimization of water networks supports the decision‐making process by identifying the optimal trade‐off between costs and performance (e.g., resilience and leakage). A major challenge in the domain of water distribution systems (WDSs) is the network (re)design. While the complex nature of WDS has already been explored with complex network analysis (CNA), literature is still lacking a CNA of optimal water networks. Based on a systematic CNA of Pareto‐optimal solutions of different WDSs, several graph characteristics are identified, and a newly developed CNA design approach for WDSs is proposed. The results show that obtained designs are comparable with results found by evolutionary optimization, but the CNA approach is applicable for large networks (e.g., 150,000 pipes) with a substantially reduced computational effort (runtime reduction up to 5 orders of magnitude). John Wiley and Sons Inc. 2020-08-18 2020-08 /pmc/articles/PMC7507211/ /pubmed/32999510 http://dx.doi.org/10.1029/2020WR027929 Text en ©2020. The Authors. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Sitzenfrei, Robert
Wang, Qi
Kapelan, Zoran
Savić, Dragan
Using Complex Network Analysis for Optimization of Water Distribution Networks
title Using Complex Network Analysis for Optimization of Water Distribution Networks
title_full Using Complex Network Analysis for Optimization of Water Distribution Networks
title_fullStr Using Complex Network Analysis for Optimization of Water Distribution Networks
title_full_unstemmed Using Complex Network Analysis for Optimization of Water Distribution Networks
title_short Using Complex Network Analysis for Optimization of Water Distribution Networks
title_sort using complex network analysis for optimization of water distribution networks
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507211/
https://www.ncbi.nlm.nih.gov/pubmed/32999510
http://dx.doi.org/10.1029/2020WR027929
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