Adaptive Multipopulation Evolutionary Algorithm for Contamination Source Identification in Water Distribution Systems
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 5
Abstract
Real-time monitoring of drinking water in a water distribution system (WDS) can effectively warn of and reduce safety risks. One of the challenges is to identify the contamination source through these observed data due to the real-time, nonuniqueness, and large-scale characteristics. To address the real-time and nonuniqueness challenges, we propose an adaptive multipopulation evolutionary optimization algorithm to determine the real-time characteristics of contamination sources, where each population aims to locate and track a different global optimum. The algorithm adaptively adjusts the number of populations using a feedback learning mechanism. To effectively locate an optimal solution for a population, a coevolutionary strategy is used to identify the location and the injection profile separately. Experimental results from three WDS networks show that the proposed algorithm is competitive in comparison with three other state-of-the-art evolutionary algorithms.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies. The source code for all the involved algorithms in this paper is available in OFEC, which is an open framework for evolutionary computation, at the link https://github.com/Changhe160/OFEC.
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 62076226, 61673355, and 61673331; by the Fundamental Research Funds for the Central Universities China University of Geosciences (Wuhan) under Grant Nos. CUG170603 and CUGGC02; by the Hubei Provincial Natural Science Foundation of China under Grant No. 2015CFA010; by the 111 project under Grant No. B17040; by the European Union’s Horizon 2020 research and innovation program under Grant No. 739551 (KIOS CoE); and by the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.
References
Berry, J., W. Hart, C. Phillips, J. Uber, and J. Watson. 2006. “Sensor placement in municipal water networks with temporal integer programming models.” J. Water Resour. Plan. Manage. 132 (4): 218–224. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:4(218).
Costa, D. M., L. F. Melo, and F. G. Martins. 2013. “Localization of contamination sources in drinking water distribution systems: A method based on successive positive readings of sensors.” Water Resour. Manage. 27 (13): 4623–4635. https://doi.org/10.1007/s11269-013-0431-z.
Cristo, C. D., and A. Leopardi. 2008. “Pollution source identification of accidental contamination in water distribution networks.” J. Water Resour. Plann. Manage. 134 (2): 197–202. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(197).
Guan, J., M. M. Aral, M. L. Maslia, and W. M. Grayman. 2006. “Identification of contaminant sources in water distribution systems using simulation-optimization method: Case study.” J. Water Resour. Plann. Manage. 132 (4): 252–262. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:4(252).
Guneshwor, L., T. I. Eldho, and A. V. Kumar. 2018. “Identification of groundwater contamination sources using meshfree RPCM simulation and particle swarm optimization.” Water Resour. Manage. 32 (4): 1517–1538. https://doi.org/10.1007/s11269-017-1885-1.
Hu, C., J. Zhao, X. Yan, D. Zeng, and S. Guo. 2015. “A MapReduce based parallel niche genetic algorithm for contaminant source identification in water distribution network.” Ad Hoc Networks 35 (Dec): 116–126. https://doi.org/10.1016/j.adhoc.2015.07.011.
Huang, J. J., and E. A. Mcbean. 2009. “Data mining to identify contaminant event locations in water distribution systems.” J. Water Resour. Plann. Manage. 135 (6): 466–474. https://doi.org/10.1061/(ASCE)0733-9496(2009)135:6(466).
Laird, C. D., L. T. Biegler, B. G. V. B. Waanders, and R. A. Bartlett. 2005. “Contamination source determination for water networks.” J. Water Resour. Plann. Manage. 131 (2): 125–134. https://doi.org/10.1061/(ASCE)0733-9496(2005)131:2(125).
Li, C., T. T. Nguyen, M. Yang, M. Mavrovouniotis, and S. Yang. 2016. “An adaptive multipopulation framework for locating and tracking multiple optima.” IEEE Trans. Evol. Comput. 20 (4): 590–605. https://doi.org/10.1109/TEVC.2015.2504383.
Li, C., T. T. Nguyen, M. Yang, S. Yang, and S. Zeng. 2015. “Multi-population methods in unconstrained continuous dynamic environments: The challenges.” Inf. Sci. 296 (Mar): 95–118. https://doi.org/10.1016/j.ins.2014.10.062.
Li, C., and S. Yang. 2012. “A general framework of multipopulation methods with clustering in undetectable dynamic environments.” IEEE Trans. Evol. Comput. 16 (4): 556–577. https://doi.org/10.1109/TEVC.2011.2169966.
Liu, L., and S. R. Ranjithan. 2010. “An adaptive optimization technique for dynamic environments.” Eng. Appl. Artif. Intell. 23 (5): 772–779. https://doi.org/10.1016/j.engappai.2010.01.007.
Liu, L., S. R. Ranjithan, and G. Mahinthakumar. 2011. “Contamination source identification in water distribution systems using an adaptive dynamic optimization procedure.” J. Water Resour. Plann. Manage. 137 (2): 183–192. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000104.
Liu, L., E. M. Zechman, E. D. Brill, Jr., G. Mahinthakumar, S. Ranjithan, and J. Uber. 2008. “Adaptive contamination source identification in water distribution systems using an evolutionary algorithm-based dynamic optimization procedure.” In Proc., Water Distribution Systems Analysis Symp. 2006, 1–9. Reston, VA: ASCE.
Liu, L., E. M. Zechman, G. Mahinthakumar, and S. Ranji Ranjithan. 2012. “Identifying contaminant sources for water distribution systems using a hybrid method.” Civ. Eng. Environ. Syst. 29 (2): 123–136. https://doi.org/10.1080/10286608.2012.663360.
Oliker, N., and A. Ostfeld. 2014. “A coupled classification—Evolutionary optimization model for contamination event detection in water distribution systems.” Water Res. 51 (Mar): 234–245. https://doi.org/10.1016/j.watres.2013.10.060.
Ostfeld, A., and E. Salomons. 2004. “Optimal layout of early warning detection stations for water distribution systems security.” J. Water Resour. Plann. Manage. 130 (5): 377–385. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:5(377).
Ostfeld, A., and E. Salomons. 2005. “Optimal early warning monitoring system layout for water networks security: Inclusion of sensors sensitivities and response delays.” Civ. Eng. Environ. Syst. 22 (3): 151–169. https://doi.org/10.1080/10286600500308144.
Perelman, L., and A. Ostfeld. 2013. “Bayesian networks for source intrusion detection.” J. Water Resour. Plann. Manage. 139 (4): 426–432. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000288.
Potter, M. A., and K. A. De Jong. 1994. “A cooperative coevolutionary approach to function optimization.” In Proc., Int. Conf. on Parallel Problem Solving from Nature, 249–257. Berlin: Springer.
Preis, A., and A. Ostfeld. 2006. “Contamination source identification in water systems: A hybrid model trees-linear programming scheme.” J. Water Resour. Plann. Manage. 132 (4): 263–273. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:4(263).
Preis, A., and A. Ostfeld. 2008. “Genetic algorithm for contaminant source characterization using imperfect sensors.” Civ. Eng. Environ. Syst. 25 (1): 29–39. https://doi.org/10.1080/10286600701695471.
Propato, M. 2006. “Contamination warning in water networks: General mixed-integer linear models for sensor location design.” J. Water Resour. Plann. Manage. 132 (4): 225–233. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:4(225).
Qin, A. K., V. L. Huang, and P. N. Suganthan. 2009. “Differential evolution algorithm with strategy adaptation for global numerical optimization.” IEEE Trans. Evol. Comput. 13 (2): 398–417. https://doi.org/10.1109/TEVC.2008.927706.
Rossman, L. A. 2000. Epanet 2 users’ manual. Washington, DC: USEPA.
Sankary, N., and A. Ostfeld. 2018. “Multiobjective optimization of inline mobile and fixed wireless sensor networks under conditions of demand uncertainty.” J. Water Resour. Plann. Manage. 144 (8): 04018043. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000930.
Seth, A., K. A. Klise, J. D. Siirola, T. Haxton, and C. D. Laird. 2016. “Testing contamination source identification methods for water distribution networks.” J. Water Resour. Plann. Manage. 142 (4): 04016001. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000619.
Shang, F., J. G. Uber, and M. M. Polycarpou. 2002. “Particle backtracking algorithm for water distribution system analysis.” J. Environ. Eng. 128 (5): 441–450. https://doi.org/10.1061/(ASCE)0733-9372(2002)128:5(441).
Sreepathi, S., K. Mahinthakumar, E. Zechman, R. Ranjithan, D. Brill, X. Ma, and G. Von Laszewski. 2007. “Cyberinfrastructure for contamination source characterization in water distribution systems.” In Proc., Int. Conf. on Computational Science, 1058–1065. New York: Springer.
Storn, R., and K. Price. 1997. “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces.” J. Global Optim. 11 (4): 341–359. https://doi.org/10.1023/A:1008202821328.
Taormina, R., and S. Galelli. 2018. “Deep-learning approach to the detection and localization of cyber-physical attacks on water distribution systems.” J. Water Resour. Plann. Manage. 144 (10): 04018065. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000983.
Xia, Y., and C. Li. 2016. “Memory-based statistical learning for the travelling salesman problem.” In Proc., 2016 IEEE Congress on Evolutionary Computation (CEC), 2935–2941. New York: IEEE.
Xin, K., X. Sheng, T. Tao, and N. Xiang. 2013. “Contamination source identification in water distribution networks based on negative gradient method.” J. Tongji Univ. (Nat. Sci.) 41 (1): 116–120.
Yan, X., J. Zhao, C. Hu, and Q. Wu. 2016. “Contaminant source identification in water distribution network based on hybrid encoding.” J. Comput. Methods Sci. Eng. 16 (2): 379–390. https://doi.org/10.3233/JCM-160625.
Yang, M., M. Omidvar, C. Li, X. Li, Z. Cai, B. Kazimipour, and X. Yao. 2017. “Efficient resource allocation in cooperative co-evolution for large-scale global optimization.” IEEE Trans. Evol. Comput. 21 (4): 493–505. https://doi.org/10.1109/TEVC.2016.2627581.
Yang, S., and C. Li. 2010. “A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments.” IEEE Trans. Evol. Comput. 14 (6): 959–974. https://doi.org/10.1109/TEVC.2010.2046667.
Yang, X., D. L. Boccelli, and A. E. De Sanctis. 2011. “A Bayesian approach for probabilistic contamination source identification.” In Proc., World Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability, 304–313. Reston, VA: ASCE.
Zechman, E. M., and S. R. Ranjithan. 2009. “Evolutionary computation-based methods for characterizing contaminant sources in a water distribution system.” J. Water Resour. Plann. Manage. 135 (5): 334–343. https://doi.org/10.1061/(ASCE)0733-9496(2009)135:5(334).
Zierolf, M. L., M. M. Polycarpou, and J. G. Uber. 1998. “Development and autocalibration of an input-output model of chlorine transport in drinking water distribution systems.” IEEE Trans. Control Syst. Technol. 6 (4): 543–553. https://doi.org/10.1109/87.701351.
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Journal of Water Resources Planning and Management
Volume 147 • Issue 5 • May 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Sep 4, 2019
Accepted: Nov 11, 2020
Published online: Feb 24, 2021
Published in print: May 1, 2021
Discussion open until: Jul 24, 2021
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