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Cluster Computing
Published in: Cluster Computing 3/2019

10-01-2018

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

Authors: Xuesong Yan, Tian Li, Chengyu Hu, Qinghua Wu

Published in: Cluster Computing | Special Issue 3/2019

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Abstract

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.
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Literature
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)
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)
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)
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)
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)
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)
7.
Perelman, L., Ostfeld, A.: Bayesian networks for source intrusion detection. J. Water Res. Plan. Manag. 139(4), 426–432 (2012)
8.
Wang, H., Jin, X.: Characterization of groundwater contaminant source using Bayesian method. Stoch. Env. Res. Risk Assess. 27(4), 867–876 (2013)
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)
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)
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)
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)
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)
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)
15.
16.
17.
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)
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)
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)MathSciNetMATH
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)
22.
Gong, W., Cai, Z.: Parameter extraction of solar cell models using repaired adaptive differential evolution. Sol. Energy 94, 209–220 (2013)
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)
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)
25.
Tang, K., Peng, F., Chen, G., Yao, X.: Population-based algorithm portfolios with automated constituent algorithms selection. Inf. Sci. 279, 94–104 (2014)
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)
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)
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)
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)
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)
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)MathSciNet
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)MathSciNet
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)
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)
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)
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)
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)
38.
Tang, K., Yang, P., Yao, X.: Negatively correlated search. IEEE J. Sel. Areas Commun. 34(3), 542–550 (2016)
39.
Wu, Q., Liu, H., Yan, X.: Multi-label classification algorithm research based on swarm intelligence. Clust. Comput. 19(4), 2075–2085 (2016)
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)
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)
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)
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)
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)
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)
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)
47.
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)
49.
Buchberger, S.G., Wu, L.: Model for instantaneous residential water demands. J. Hydraul. Eng. 121(3), 232–246 (1995)
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)
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)
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)
53.
Yan, X., Liu, H., Zhu, Z., Wu, Q.: Hybrid genetic algorithm for engineering design problems. Clust. Comput. 20(1), 263–275 (2017)
Metadata
Title
Real-time localization of pollution source for urban water supply network in emergencies
Authors
Xuesong Yan
Tian Li
Chengyu Hu
Qinghua Wu
Publication date
10-01-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 3/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-1725-y

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