Abstract
A novel network partition model is presented within the water distribution system (WDS). Firstly, random walk community detection (RWCD) is employed to divide WDS into different partitions concerning the average pressure of nodes. Then, network reliability is assessed based on hydraulic reliability estimation (HRE), mechanical reliability estimation (MRE), flow entropy function (FEF), and network resilience (NR), via optimizing boundary pipes by the non-dominated sorting genetic algorithm-II (NSGA-II). Finally, pressure-reducing valves (PRVs) are set to pipes for acquiring optimized partitions. The Open Water Analytics (OWA) toolbox and Matlab-2018b is adopted as a hydraulic calculation tool for these extended period simulations (EPS). Seven cases of WDSs were used to verify the practicability of this model. The results demonstrate that network reliability is improved effectively after partitioning and optimizing.
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References
Amin S, Litrico X, Sastry SS, Bayen AM (2013) Cyber security of water SCADA Systems—Part II: attack detection using enhanced hydrodynamic models. IEEE Trans Control Syst Technol 21:1679–1693. https://doi.org/10.1109/TCST.2012.2211874
Bentley Systems (2013) WaterGEMS V8i users’manual. Watertown, CT
Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008:P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Ciaponi C, Murari E, Todeschini S (2016) Modularity-Based Procedure for Partitioning Water Distribution Systems into Independent Districts. Water Resour Manag 30:2021–2036. https://doi.org/10.1007/s11269-016-1266-1
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197. https://doi.org/10.1109/4235.996017
Dehghanian N, Nadoushani SSM, Saghafian B, Akhtari R (2019) Performance evaluation of a fuzzy hybrid clustering technique to identify flood source areas. Water Resour Manag 33:4621–4636. https://doi.org/10.1007/s11269-019-02385-7
Delvenne J-C, Yaliraki SN, Barahona M (2009) Stability of graph communities across time scales. http://arxiv.org/abs/0812.1811
Delvenne J-C, Schaub MT, Yaliraki SN, Barahona M (2013) The stability of a graph partition: a dynamics-based framework for community detection. pp 221–242. https://doi.org/10.1007/978-1-4614-6729-8_11
Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174. https://doi.org/10.1016/j.physrep.2009.11.002
Ghodrati A, Lotfi S (2012) A Hybrid CS/GA algorithm for global optimization. pp 397–404. https://doi.org/10.1007/978-81-322-0487-9_38
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99:7821–7826. https://doi.org/10.1073/pnas.122653799
Giudicianni C, Herrera M, di Nardo A, Adeyeye K (2020) Automatic multiscale approach for water networks partitioning into dynamic district metered areas. Water Resour Manag 34:835–848. https://doi.org/10.1007/s11269-019-02471-w
Gupta R, Bhave PR (1994) Reliability analysis of water-distribution systems. J Environ Eng 120:447–461. https://doi.org/10.1061/(ASCE)0733-9372(1994)120:2(447)
Gupta A, Kulat KD (2018) A selective literature review on leak management techniques for water distribution system. Water Resour Manag 32:3247–3269. https://doi.org/10.1007/s11269-018-1985-6
Hwang H, Lansey K (2017) Water distribution system classification using system characteristics and graph-theory metrics. J Water Resour Plan Manag 143:04017071. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000850
Jolly MD, Lothes AD, Sebastian Bryson L, Ormsbee L (2014) Research database of water distribution system models. J Water Resour Plan Manag 140:410–416. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000352
Kumar A, Kumar P, Singh VK (2019) Evaluating different machine learning models for runoff and suspended sediment simulation. Water Resour Manag 33:1217–1231. https://doi.org/10.1007/s11269-018-2178-z
Lambiotte R, Delvenne JC, Barahona M (2014) Random walks, Markov processes and the multiscale modular organization of complex networks. IEEE Trans Netw Sci Eng 1:76–90. https://doi.org/10.1109/TNSE.2015.2391998
Martelot EL, Hankin C (2012) Multi-scale community detection using stability optimisation within greedy algorithms. http://arxiv.org/abs/1201.3307
McDonnell BE, Salomons E, Uber J, Klise K (2007) Open Water Analytics (OWA). KIOS Research Center for Intelligent Systems and Networks of the University of Cyprus. http://wateranalytics.org/
Mounce SR, Ellis K, Edwards JM et al (2017) Ensemble decision tree models using RUSBoost for Estimating risk of iron failure in drinking water distribution systems. Water Resour Manag 31:1575–1589. https://doi.org/10.1007/s11269-017-1595-8
Ostfeld A, Uber JG, Salomons E et al (2008) The Battle of the Water Sensor Networks (BWSN): A design challenge for engineers and algorithms. J Water Resour Plan Manag 134:556–568. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:6(556)
Pasha MFK, Lansey K (2014) Strategies to develop warm solutions for real-time pump scheduling for water distribution systems. Water Resour Manag 28:3975–3987. https://doi.org/10.1007/s11269-014-0721-0
Prasad TD, Park N-S (2004) Multiobjective genetic algorithms for design of water distribution networks. J Water Resour Plan Manag 130:73–82. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:1(73)
Rodrigues GPW, Costa LHM, Farias GM, de Castro MAH (2019) A depth-first search algorithm for optimizing the gravity pipe networks layout. Water Resour Manag 33:4583–4598. https://doi.org/10.1007/s11269-019-02373-x
Rossman L (2000) EPANET’s user manual. US EPA, Washington, DC. https://nepis.epa.gov/Adobe/PDF/P1007WWU.pdf
Savic DA, Walters GA, Schwab M (1997) Multiobjective genetic algorithms for pump scheduling in water supply. pp 227–235. https://doi.org/10.1007/BFb0027177
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Sirsant S, Reddy MJ (2020) Assessing the performance of surrogate measures for water distribution network reliability. J Water Resour Plan Manag 146:1–15. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001244
Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2:221–248. https://doi.org/10.1162/evco.1994.2.3.221
Su Y, Mays LW, Duan N, Lansey KE (1987) Reliability-based optimization model for water distribution systems. J Hydraul Eng 113:1539–1556. https://doi.org/10.1061/(ASCE)0733-9429(1987)113:12(1539)
Tanyimboh TT, Templeman AB (1993) Maximum entropy flows for single-source networks. Eng Optim 22:49–63. https://doi.org/10.1080/03052159308941325
Todini E (2000) Looped water distribution networks design using a resilience index based heuristic approach. Urban Water 2:115–122. https://doi.org/10.1016/S1462-0758(00)00049-2
Ulanicki B, Shipley N, Prescott S (2003) Analysis of district metered area (DMA) performance. In: Advances in Water Supply Management. Taylor & Francis, Abingdon
Wu ZY, Rahman A (2017) Optimized deep learning framework for water distribution data-driven modeling. Procedia Eng 186:261–268. https://doi.org/10.1016/j.proeng.2017.03.240
Yang X-S (2014) Nature-inspired optimization algorithms. Elsevier. https://doi.org/10.1016/C2013-0-01368-0
Zhang M, Shen Y (2007) Study and application of steady flow and unsteady flow mathematical model for channel networks. J Hydrodyn 19:572–578. https://doi.org/10.1016/S1001-6058(07)60155-3
Zhang Q, Wu ZY, Zhao M et al (2016) Leakage zone identification in large-scale water distribution systems using multiclass support vector machines. J Water Resour Plan Manag 142:04016042. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000661
Zhuan X, Xia X (2013) Optimal operation scheduling of a pumping station with multiple pumps. Appl Energy 104:250–257. https://doi.org/10.1016/j.apenergy.2012.10.028
Funding
This work was supported by the Shenzhen Science and Technology Plan Program (KJYY20170413170501147) and the Major Science and Technology Program for Water Pollution Control and Treatment (2014ZX07406-003).
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Tianwei Mu: Conceptualization, Methodology, Software, Writing - Original Draft.
Yan Lu: Writing - Review & Editing.
Haoqiang Tan: Project administration, Supervision, Resources, Validation, Funding acquisition.
Haowen Zhang: Data curation, Visualization.
Chengzhi Zheng: Investigation, Inspection, Funding acquisition.
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Mu, T., Lu, Y., Tan, H. et al. Random Walks Partitioning and Network Reliability Assessing in Water Distribution System. Water Resour Manage 35, 2325–2341 (2021). https://doi.org/10.1007/s11269-021-02793-8
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DOI: https://doi.org/10.1007/s11269-021-02793-8