Cluster Computing

, Volume 22, Supplement 3, pp 5941–5954 | Cite as

Real-time localization of pollution source for urban water supply network in emergencies

  • Xuesong Yan
  • Tian Li
  • Chengyu Hu
  • Qinghua WuEmail author


In recent years, drinking water pollution happens from time to time, severely endangering social stability and residents’ life. By placing sensors in urban water supply pipes to monitor water quality in real time, the negative impacts of pollution accidents can be reduced to a great degree. However, how to localize pollution source in real time according to the detection information from water quality sensors is still a challenging topic. The difficulty is that when the pollution is detected, the pollution information collected by sensors is insufficient to localize the pollution source. This paper mainly studies the real-time localization of the source of paroxysmal pollution when water demand is uncertain. Many previous studies adopted the simulation-optimization method to simulate pollution event in a fixed period of time after it happens, then localize the pollution source reversely using all the simulation data; however, when the pollution source has been detected, real-time localization by simulation-optimization method can shorten the time of simulation and thus deal with the pollution in real time to reduce its harm. This paper first describes the problem of real-time paroxysmal pollution source localization and provides the diagram of simulation-optimization model to the problem; then the design of real-time localization algorithm is proposed with consideration of objective function; at last, experiments are carried out on pipe networks of different scales, and the results show that compared to traditional pollution source localization method, the real-time pollution source localization method can find the true pollution event with less sensor data in a shorter period of time.


Real-time localization of pollution source Simulation-optimization Genetic algorithm Objective function 



This paper is supported by Natural Science Foundation of China (No. 61673354), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) and the State Key Lab of Digital Manufacturing Equipment & Technology.


  1. 1.
    Ding, F., Huang, L., Wang, R., et al.: Water pollution emergencies in China 2004–2005: monitoring data analysis. Chin. J. Public Health 4, 1–15 (2017)Google Scholar
  2. 2.
    Shang, F., Uber, J.G., Polycarpou, M.M.: Particle backtracking algorithm for water distribution system analysis. J. Environ. Eng. 128(5), 441–450 (2002)Google Scholar
  3. 3.
    Laird, C.D., Biegler, L.T., van Bloemen Waanders, B.G., Bartlett, R.A.: Contamination source determination for water networks. J. Water Res. Plan. Manag. 131(2), 125–134 (2005)Google Scholar
  4. 4.
    De Sanctis, A.E., Shang, F., Uber, J.G.: Real-time identification of possible contamination sources using network backtracking methods. J. Water Res. Plan. Manag. 136(4), 444–453 (2009)Google Scholar
  5. 5.
    Costa, D.M., Melo, L.F., Martins, F.G.: Localization of contamination sources in drinking water distribution systems: a method based on successive positive readings of sensors. Water Resour. Manag. 27(13), 4623–4635 (2013)Google Scholar
  6. 6.
    Huang, J.J., McBean, E.A.: Data mining to identify contaminant event locations in water distribution systems. J. Water Res. Plan. Manag. 135(6), 466–474 (2009)Google Scholar
  7. 7.
    Perelman, L., Ostfeld, A.: Bayesian networks for source intrusion detection. J. Water Res. Plan. Manag. 139(4), 426–432 (2012)Google Scholar
  8. 8.
    Wang, H., Jin, X.: Characterization of groundwater contaminant source using Bayesian method. Stoch. Env. Res. Risk Assess. 27(4), 867–876 (2013)Google Scholar
  9. 9.
    Wang, H., Harrison, K.W.: Improving efficiency of the Bayesian approach to water distribution contaminant source characterization with support vector regression. J. Water Res. Plan. Manag. 140(1), 3–11 (2014)Google Scholar
  10. 10.
    Guan, J., Aral, M.M., Maslia, M.L., Grayman, W.M.: Identification of contaminant sources in water distribution systems using simulation-optimization method: case study. J. Water Res. Plan. Manag. 132(4), 252–262 (2006)Google Scholar
  11. 11.
    Liu, L., Ranjithan, S.R., Mahinthakumar, G.: Contamination source identification in water distribution systems using an adaptive dynamic optimization procedure. J. Water Res. Plan. Manag. 137(2), 183–192 (2010)Google Scholar
  12. 12.
    Hu, C., Zhao, J., Yan, X., Zeng, D., Guo, S.: A MapReduce based parallel niche genetic algorithm for contaminant source identification in water distribution network. Ad Hoc Netw. 35, 116–126 (2015)Google Scholar
  13. 13.
    Yan, X., Zhao, J., Hu, C., Wu, Q.: Contaminant source identification in water distribution network based on hybrid encoding. J. Comput. Methods Sci. Eng. 16(2), 379–390 (2016)Google Scholar
  14. 14.
    Yan, X., Sun, J., Hu, C.: Research on contaminant sources identification of uncertainty water demand using genetic algorithm. Clust. Comput. 20(2), 1007–1016 (2017)Google Scholar
  15. 15.
    Yan, X., Zhao, J., Hu, C., Zeng, D.: Multimodal Optimization Problem in Contamination Source Determination of Water Supply Networks. Swarm Evol. Comput. (2017). Google Scholar
  16. 16.
    Yan, X., Gong, W., Wu, Q.: Contaminant source identification of water distribution networks using cultural algorithm. Concur. Comput. (2017), Google Scholar
  17. 17.
    Yan, X., Zhu, Z., Li, T.: Pollution source localization in an urban water supply network based on dynamic water demand. Environ. Sci. Pollut. Res. (2017). Google Scholar
  18. 18.
    Liu, B., Wang, L., Jin, Y.H.: An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Transact. Syst. Man Cybern. 37(1), 18–27 (2007)Google Scholar
  19. 19.
    Xing, L., Rohlfshagen, P., Chen, Y., Yao, X.: An evolutionary approach to the multidepot capacitated arc routing problem. IEEE Trans. Evol. Comput. 14(3), 356–374 (2010)Google Scholar
  20. 20.
    Wang, L., Pan, Q.K., Suganthan, P.N., Wang, W.H., Wang, Y.M.: A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems. Comput. Operat. Res. 37(3), 509–520 (2010)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol. Comput. 1(1), 32–49 (2011)Google Scholar
  22. 22.
    Gong, W., Cai, Z.: Parameter extraction of solar cell models using repaired adaptive differential evolution. Sol. Energy 94, 209–220 (2013)Google Scholar
  23. 23.
    Wang, R., Purshouse, R.C., Fleming, P.J.: Preference-inspired coevolutionary algorithms for many-objective optimization. IEEE Trans. Evol. Comput. 17(4), 474–494 (2013)Google Scholar
  24. 24.
    Gong, M., Cai, Q., Chen, X., Ma, L.: Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans. Evol. Comput. 18(1), 82–97 (2014)Google Scholar
  25. 25.
    Tang, K., Peng, F., Chen, G., Yao, X.: Population-based algorithm portfolios with automated constituent algorithms selection. Inf. Sci. 279, 94–104 (2014)Google Scholar
  26. 26.
    Gong, W., Zhou, A., Cai, Z.: A multioperator search strategy based on cheap surrogate models for evolutionary optimization. IEEE Trans. Evol. Comput. 19(5), 746–758 (2015)Google Scholar
  27. 27.
    Kumar, N., Singh, J.P., Bali, R.S., Misra, S., Ullah, S.: An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing. Clust. Comput. 18(3), 1263–1283 (2015)Google Scholar
  28. 28.
    Zhou, A., Sun, J., Zhang, Q.: An estimation of distribution algorithm with cheap and expensive local search methods. IEEE Trans. Evol. Comput. 19(6), 807–822 (2015)Google Scholar
  29. 29.
    Jiao, H., Zhang, J., Li, J., Shi, J., Li, J.: Immune optimization of task scheduling on multidimensional QoS constraints. Clust. Comput. 18(2), 909–918 (2015)Google Scholar
  30. 30.
    Gong, W., Yan, X., Liu, X., Cai, Z.: Parameter extraction of different fuel cell models with transferred adaptive differential evolution. Energy 86, 139–151 (2015)Google Scholar
  31. 31.
    Wang, L., Ni, H., Yang, R., Pardalos, P.M., Du, X., Fei, M.: An adaptive simplified human learning optimization algorithm. Inf. Sci. 320, 126–139 (2015)MathSciNetGoogle Scholar
  32. 32.
    Gong, M., Liu, J., Li, H., Cai, Q., Su, L.: A multiobjective sparse feature learning model for deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 26(12), 3263–3277 (2015)MathSciNetGoogle Scholar
  33. 33.
    Gong, W., Cai, Z., Liang, D.: Adaptive ranking mutation operator based differential evolution for constrained optimization. IEEE Trans. Cybern. 45(4), 716–727 (2015)Google Scholar
  34. 34.
    Yang, S., Yang, M., Wang, S., Huang, K.: Adaptive immune genetic algorithm for weapon system portfolio optimization in military big data environment. Clust. Comput. 19(3), 1359–1372 (2016)Google Scholar
  35. 35.
    Gong, M., Zhang, M., Yuan, Y.: Unsupervised band selection based on evolutionary multiobjective optimization for hyperspectral images. IEEE Trans. Geosci. Remote Sens. 54(1), 544–557 (2016)Google Scholar
  36. 36.
    Zhou, A., Zhang, Q.: Are all the subproblems equally important? Resource allocation in decomposition-based multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 20(1), 52–64 (2016)Google Scholar
  37. 37.
    Yan, X., Wu, Q., Sheng, V.S.: A double weighted Naive Bayes with niching cultural algorithm for multi-label classification. Int. J. Pattern Recognit. Artif. Intell. 30(06), 1650013 (2016)Google Scholar
  38. 38.
    Tang, K., Yang, P., Yao, X.: Negatively correlated search. IEEE J. Sel. Areas Commun. 34(3), 542–550 (2016)Google Scholar
  39. 39.
    Wu, Q., Liu, H., Yan, X.: Multi-label classification algorithm research based on swarm intelligence. Clust. Comput. 19(4), 2075–2085 (2016)Google Scholar
  40. 40.
    Deng, J., Wang, L.: A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm Evol. Comput. 32, 121–131 (2017)Google Scholar
  41. 41.
    Wang, R., Xiong, J., Ishibuchi, H., Wu, G., Zhang, T.: On the effect of reference point in MOEA/D for multi-objective optimization. Appl. Soft Comput. 58, 25–34 (2017)Google Scholar
  42. 42.
    Wu, Q., Wang, L., Zhu, Z.: Research of pre-stack AVO elastic parameter inversion problem based on hybrid genetic algorithm. Clust. Comput. 20(4), 3173–3783 (2017)Google Scholar
  43. 43.
    Tang, K., Wang, J., Li, X., Yao, X.: A scalable approach to capacitated arc routing problems based on hierarchical decomposition. IEEE Tans. Cybern. 47(11), 3928–3940 (2017)Google Scholar
  44. 44.
    Yan, X., Song, T., Wu, Q.: An improved cultural algorithm and its application in image matching. Multimed. Tools Appl. 76(13), 14951–14968 (2017)Google Scholar
  45. 45.
    Gong, W., Wang, Y., Cai, Z., Yang, S.: A weighted biobjective transformation technique for locating multiple optimal solutions of nonlinear equation systems. IEEE Trans. Evol. Comput. 21(5), 697–713 (2017)Google Scholar
  46. 46.
    Wu, Q., Zhu, Z., Yan, X.: Research on the parameter inversion problem of prestack seismic data based on improved differential evolution algorithm. Clust. Comput. 20(4), 2881–2890 (2017)Google Scholar
  47. 47.
    Xing, L., Li, W., He, M., Tan, X.: Comprehensive multic - objective model to remote sensing data processing task scheduling problem. Concurr. Comput. 29(24) (2017), Google Scholar
  48. 48.
    Yan, X., Zhu, Z., Wu, Q.: Intelligent inversion method for pre-stack seismic big data based on MapReduce. Comput. Geosci. 110, 81–89 (2018)Google Scholar
  49. 49.
    Buchberger, S.G., Wu, L.: Model for instantaneous residential water demands. J. Hydraul. Eng. 121(3), 232–246 (1995)Google Scholar
  50. 50.
    Rossman, L.A.: EPANET 2 User’s Manual, Water Supply And Water Resources Division. National Risk Management Research Laboratory, US Environmental Protection Agency, Cincinnati (2000)Google Scholar
  51. 51.
    Kim, N., Heo, M., Fleysher, R., Branch, C.A., Lipton, M.L.: Two step Gaussian mixture model approach to characterize white matter disease based on distributional changes. J. Neurosci. Methods 270, 156–164 (2016)Google Scholar
  52. 52.
    Vankayala, P., Sankarasubramanian, A., Ranjithan, S.R., Mahinthakumar, G.: Contaminant source identification in water distribution networks under conditions of demand uncertainty. Environ. Forensics 10(3), 253–263 (2009)Google Scholar
  53. 53.
    Yan, X., Liu, H., Zhu, Z., Wu, Q.: Hybrid genetic algorithm for engineering design problems. Clust. Comput. 20(1), 263–275 (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.State Key Lab of Digital Manufacturing Equipment & TechnologyWuhanChina
  3. 3.Faculty of Computer Science and EngineeringWuHan Institute of TechnologyWuhanChina

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