Hybrid computing intelligent system for assessing the stability of the water distribution system and determining the optimum locations of pressure sensors
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2026.2.06Keywords:
sensor placement, WDN, artificial intelligence, hydraulic modeling, ANFIS, Mamdani, genetic algorithms, EPANET 2.2Abstract
This study introduces a method for determining the best locations for pressure sensors in water supply networks and for assessing network conditions using artificial intelligence techniques. The goal is to identify the network nodes that would provide the most important information for detecting water leaks and evaluating the overall network status. The selection of sensor locations was based on data sets of pressure changes caused by various leak scenarios generated by EPANET simulations. Genetic algorithms were used to rank candidate nodes and determine the optimal number of sensor locations. The next step involved assessing the network state using the ANFIS neuro-fuzzy network and the Mamdani neuro-fuzzy logical inference algorithm. These algorithms were implemented in the Google Colab environment and tested on a section of the water supply network in Kyiv, Ukraine.
References
X. Zhou et al., “Deep learning identifies accurate burst locations in water distribution networks,” Water Research, vol. 166, 115058, 2019. doi: https://doi.org/10.1016/j.watres.2019.115058
E. Creaco, M. Blokker, S. Buchberger, “Models for generating household water demand pulses: Literature review and comparison,” Journal of Water Resources Planning and Management, 143(6), 04017013, 2017. doi: https://doi.org/10.1061/(ASCE)WR.1943-5452.0000763
Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga. Meta-heuristic and Evolutionary Algorithms for Engineering Optimization. John Wiley & Sons, Inc., 2017. doi: https://doi.org/10.1002/9781119387053
C.P. Liou, “Limitations and proper use of the Hazen–Williams equation,” Journal of Hydraulic Engineering, 124(9), pp. 951–954, 1998. doi: https://doi.org/10.1061/(ASCE)0733-9429(1998)124:9(951)
Xudong Fan, Xijin Zhang, Xiong ( Bill) Yu. “Machine learning model and strategy for fast and accurate detection of leaks in water supply network,” Journal of Infrastructure Preservation and Resilience, vol. 2, article no. 10, 2021. doi: https://doi.org/10.1186/s43065-021-00021-6
A.E. Eiben, S.K. Smit, “Evolutionary algorithm parameters and methods to tune them,” in Y. Hamadi, E. Monfroy, F. Saubion (eds) Autonomous search. Springer, Berlin, Heidelberg, 2011, pp. 15–36. doi: https://doi.org/10.1007/978-3-642-21434-9
N. Irfan, M.C.E. Yagoub, K. Hettak, “Genetic algorithm based on efficient tag detection in RFID reader networks,” 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings, Ottawa, ON, Canada, 2011, pp. 1–4. doi: https://doi.org/10.1109/CIMSA.2011.6059921
D. Rani, S.K. Jain, D.K. Srivastava, M. Perumal, “Genetic algorithms and their applications to water resources systems,” in X.-S.Yang, A.H. Gandomi, S. Talatahari, A.H. Alavi (eds) Metaheuristics in water, geotechnical and transport engineering. Elsevier, 2013, pp. 43–78. doi: https://doi.org/10.1016/B978-0-12-398296-4.00003-9
A.R. Simpson, G.C. Dandy, L.J. Murphy, “Genetic algorithms compared to other techniques for pipe optimization,” Journal of Water Resources Planning and Management, 120(4), pp. 423–443, 1994. doi: https://doi.org/10.1061/(ASCE)0733-9496(1994)120:4(423)
F. Di Pierro, S.-T. Khu, D. Savić, L. Berardi, “Efficient multi-objective optimal design of water distribution networks on a budget of simulations using hybrid algorithms,” Environmental Modelling & Software, 24(2), pp. 202–213, 2009. doi: https://doi.org/10.1016/j.envsoft.2008.06.008
T.D. Prasad, N.S. Park, “Multiobjective genetic algorithms for design of water distribution networks,” Journal of Water Resources Planning and Management, 130(1), pp. 73–82, 2004. doi: https://doi.org/10.1061/(ASCE)0733-9496(2004)130:1(73)
D. Kang, K. Lansey, “Revisiting optimal water-distribution system design: Issues and a heuristic hierarchical approach,” Journal of Water Resources Planning and Management, 138(3), pp. 208–217, 2012. doi: https://doi.org/10.1061/(ASCE)WR.1943-5452.0000165
M. Cisty, Z. Bajtek, L. Celar, “A two-stage evolutionary optimization approach for an irrigation system design,” Journal of Hydroinformatics, 19(1), pp. 115–122, 2017. doi: https://doi.org/10.2166/hydro.2016.032
B. Martínez-Bahena, M.A. Cruz-Chávez, E.Y. Ávila-Melgar, M.H. Cruz-Rosales, R. Rivera-Lopez, “Using a genetic algorithm with a mathematical programming solver to optimize a real water distribution system,” Water, 10(10), 1318, 2018. doi: https://doi.org/10.3390/w10101318
W. Bi, G. Dandy, “Optimization of water distribution systems using online retrained metamodels,” Journal of Water Resources Planning and Management, 140(11), 2014. doi: https://doi.org/10.1061/(ASCE)WR.1943-5452.0000419
S. Khalifeh, S. Akbarifard, V. Khalifeh, E. Zallaghi, “Optimization of water distribution of network systems using the Harris Hawks optimization algorithm (Case study: Homashahr city),” MethodsX, vol. 7, 100948, 2020. doi: https://doi.org/10.1016/j.mex.2020.100948
M.S. Khorshidi, M.R. Nikoo, N. Taravatrooy, M. Sadegh, M. Al-Wardy, G.A. Al-Rawas, “Pressure sensor placement in water distribution networks for leak detection using a hybrid information-entropy approach,” Information Science, vol. 516, pp. 56–71, 2020. doi: https://doi.org/10.1016/j.ins.2019.12.043
N. Sho, A. Maki, O. Naoya, H. Jun, F. Naoki, “Monitoring of iodine species during water purification at a public water treatment plant in Japan,” Water Supply, 19(2), pp. 580–587, 2019. doi: https://doi.org/10.2166/ws.2018.104
P. Ami, O. Avi, “Multiobjective Contaminant Sensor Network Design for Water Distribution Systems,” Journal of Water Resources Planning and Management, 134(4), pp. 366–377, 2008. doi: https://doi.org/10.1061/(ASCE)0733-9496(2008)134:4(366)
Z. Hu, W. Chen, B. Chen, D. Tan, Y. Zhang, D. Shen, “Robust hierarchical sensor optimization placement method for leak detection in water distribution systems,” Water Resources Management, vol. 35, pp. 3995–4008, 2021. doi: https://doi.org/10.1007/s11269-021-02922-3
Y. Huang, Y. Lan, S.J. Thomson, A. Fang, W.C. Hoffmann, R.E. Lacey, “Development of soft computing and applications in agricultural and biological engineering,” Computers and Electronics in Agriculture, 71(2), pp. 107–127, 2010. doi: https://doi.org/10.1016/j.compag.2010.01.001
E.H. Mamdani, “Application of fuzzy algorithms for control of simple dynamic plant,” Proceedings of the Institution of Electrical Engineers, 121(12), 1974. doi: https://doi.org/10.1049/piee.1974.0328
L. Sela, S. Amin, “Robust sensor placement for pipeline monitoring: Mixed integer and greedy optimization,” Advanced Engineering Informatics, vol. 36, pp. 55–63, 2018. doi: https://doi.org/10.1016/j.aei.2018.02.004
R. Pérez et al., “Leak localization in water networks: A model-based methodology using pressure sensors applied to a real network in Barcelona [applications of control],” in IEEE Control Systems Magazine, vol. 34, no. 4, pp. 24–36, Aug. 2014. doi: https://doi.org/10.1109/MCS.2014.2320336
D. Kang, K. Lansey, “Optimal Meter Placement for Water Distribution System State Estimation,” Journal of Water Resources Planning and Management, 136(3), pp. 337–347, 2010. doi: https://doi.org/10.1061/(ASCE)WR.1943-5452.0000037
I. Santos-Ruiz, F.R. López-Estrada, V. Puig, J. Blesa, “Estimation of Node Pressures in Water Distribution Networks by Gaussian Process Regression,” 2019 4th Conference on Control and Fault Tolerant Systems (SysTol), Casablanca, Morocco, 2019, pp. 50–55. doi: https://doi.org/10.1109/SYSTOL.2019.8864793
J. Mankad, B. Natarajan, B. Srinivasan, “Integrated approach for optimal sensor placement and state estimation: A case study on water distribution networks,” ISA Transactions, vol. 123, pp. 272–285, 2021. doi: https://doi.org/10.1016/j.isatra.2021.06.004
S. Shin et al., “A systematic review of quantitative resilience measures for water infrastructure systems,” Water, 10(2), 164, 2018. doi: https://doi.org/10.3390/w10020164
D. Xu, Z. Lv, H. Zeng, H. Bessaih, B. Sun, “Sensor placement optimization in the artificial lateral line using optimal weight analysis combining feature distance and variance evaluation,” ISA Transactions, vol. 86, pp. 110–121, 2019. doi: https://doi.org/10.1016/j.isatra.2018.10.039
H. Dong, N. Hou, Z. Wang, W. Ren, “Variance-Constrained State Estimation for Complex Networks with Randomly Varying Topologies,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 7, pp. 2757–2768, July 2018. doi: https://doi.org/10.1109/TNNLS.2017.2700331
H. Liu, H. Yu, “Decentralized state estimation for a large-scale spatially interconnected system,” ISA Transactions, vol. 74, pp. 67–76, 2018. doi: https://doi.org/10.1016/j.isatra.2018.01.007
C. Ding, H. Peng, “Minimum redundancy feature selection from microarray gene expression data,” Journal of Bioinformatics and Computational Biology, vol. 03, no. 02, pp. 185–205, 2005. doi: https://doi.org/10.1142/S0219720005001004
L.A. Zadeh, “Fuzzy sets,” Information and Control, 8(3), pp. 338–353, 1965. doi: https://doi.org/10.1016/S0019-9958(65)90241-X
R. Mirabbasi, S.M. Mazloumzadeh, M.B. Rahnama, “Evaluation of irrigation water quality using fuzzy logic,” Research Journal of Environmental Science, 2(5), pp. 340–352, 2008. Available: https://lnk.ua/MHVvjKtE8
H. Gharibi, A.H. Mahvi, R. Nabizadeh, H. Arabalibeik, M. Yunesian, M.H. Sowlat, “A novel approach in water quality assessment based on fuzzy logic,” Journal of Environmental Management, 112, pp. 87–95, 2012. doi: https://doi.org/10.1016/j.jenvman.2012.07.007
M. Vadiati, A. Asghari-Moghaddam, M. Nakhaei, J. Adamowski, A.H. Akbarzadeh,“A fuzzy-logic-based decision-making approach for identification of groundwater quality based on groundwater quality indices,” Journal of Environmental Management, 184, pp. 255–270, 2016. doi: https://doi.org/10.1016/j.jenvman.2016.09.082
Soumaya Hajji et al., “Using a Mamdani Fuzzy Inference System Model (MFISM) for Ranking Groundwater Quality in an Agri-Environmental Context: Case of the Hammamet-Nabeul Shallow Aquifer (Tunisia),” Water, 13(18), 2507, 2021. doi: https://doi.org/10.3390/w13182507
A.S. Nasr, M. Rezaei, M.D. Barmaki, “Groundwater contamination analysis using Fuzzy Water Quality index (FWQI): Yazd province, Iran,” Geopersia, 3, pp. 47–55, 2013. Available: https://www.researchgate.net/publication/243055680_Groundwater_contamination_analysis_using_Fuzzy_Water_Quality_index_FWQI_Yazd_province_Iran#full-text
Abu Rashid, Sangeeta Kumari, “Performance evaluation of ANN and ANFIS models for estimating velocity and pressure in water distribution networks,” Water Supply, 23 (9), pp. 3925–3949, 2023. doi: https://doi.org/10.2166/ws.2023.224
L.A. Rossman, H. Woo, M. Tryby, F. Shang, R. Janke, T. Haxton, EPANET 2.2 User Manual; Technical Report EPA/600/R-20/133. U.S. Environmental Protection Agency: Washington, DC, USA, 2020.
L. Ferrandez-Gamot et al., “Leak localization in water distribution networks using pressure residuals and classifiers,” IFAC-PapersOnLine, 48(21), pp. 220–225, 2015. doi: https://doi.org/10.1016/j.ifacol.2015.09.531
J. Blank, K. Deb, “Pymoo: Multi-Objective Optimization in Python,” in IEEE Access, vol. 8, pp. 89497–89509, 2020. doi: https://doi.org/10.1109/ACCESS.2020.2990567
