Abstract
The real-time location of pollution sources is the process of inverting pollution sources based on the dynamic optimization model constructed by the time-varying pollution concentration detected by the water quality sensor. Due to the vast quantities of the water supply networks, the water quality sensors will only be placed on critical nodes, resulting in multiple solutions. However, the increased monitoring data enhances the uniqueness of the solution. Combined with the real-time location of pollution sources, this work proposed a multi-strategy dynamic multi-mode optimization algorithm based on domain knowledge, which could guide the population search and avoid trapped into local optimal. The merging mechanism was used to keep the diversity of the population and prevent sub-population clustering on the same optimal solution. The simulation results showed that the algorithm could effectively solve the real-time location problem of pollution sources in different pipe networks and pollution scenarios.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
All data generated or analysed during this study are included in this published article.
Abbreviations
- PBA:
-
Particle backtracking algorithm
- PSO:
-
Particle swarm optimization
- MSDMOA:
-
Multi-Strategy Dynamic Multi-Mode Optimization Algorithm
- DMOA:
-
Dynamic Multi-Group Optimization Algorithm
- f :
-
prediction error
- t 0 :
-
the time when the sensor first detects the pollution in the real scene
- t c :
-
the current time
- N s :
-
the number of sensors
- c it obs :
-
the pollutant concentration of the observation sensor i at time t
- c it * :
-
the pollutant concentration of sensor i when EPANET simulates potential pollution event at time t
- L :
-
the total number of nodes
- M :
-
the injection vector of the pollution source
- T 0 :
-
the initial injection time
- D :
-
the injection duration
- k :
-
Number of subpopulations
- pop_size :
-
Size of subpopulations
- g Max :
-
Population iterations
- P c :
-
Crossover probability
- P m :
-
Mutation probability
- Δt :
-
Time step
- t Max :
-
Positioning time
- N :
-
the optimal solution set size
- ε :
-
threshold
- l true :
-
actual pollution source position
- p :
-
the positioning accuracy rate
- f c :
-
the average value of successful positioning f
References
Avi O, Elad S (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
Avi O, James GU, Elad S, Berry JW, Hart WE, Phillips CA, Watson JP, Dorini G, Jonkergouw P, Kapelan Z et al (2008) The battle of the water sensor networks (bwsn): A design challenge for engineers and algorithms. J Water Resour Plan Manag 134(6):556–568. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:6(556)
Costa DM, Melo LF, Martins FG (2013) 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. https://doi.org/10.1007/s11269-013-0431-z
Cruz C, González JR, Pelta DA (2011) Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput 15(7):1427–1448. https://doi.org/10.1007/s00500-010-0681-0
De S, Annamaria E, Feng S, James GU (2010) Real-time identification of possible contamination sources using network backtracking methods. J Water Resour Plan Manag 136(4):444–453. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000050
Gong J, Yan X, Hu C, Wu Q (2019) Collaborative based pollution sources identification algorithm in water supply sensor networks. Desalin Water Treat 168:123–135. https://doi.org/10.5004/dwt.2019.24204
Gu X, Zhang Q, Li J, Vijay PS, Liu J, Sun P, He C, Wu J (2019a) Intensification and expansion of soil moisture drying in warm season over eurasia under global warming. J Geophys Res-Atmos 124:3765–3782. https://doi.org/10.1029/2018JD029776
Gu X, Zhang Q, Li J, Vijay PS, Liu J, Sun P, Cheng C (2019b) Attribution of global soil moisture drying to human activities: A quantitative viewpoint. Geophys Res Lett 46:2573–2582. https://doi.org/10.1029/2018GL080768
Gu X, Zhang Q, Li J, Vijay PS, Sun P (2019c) Impact of urbanization on nonstationarity of annual and seasonal precipitation extremes in China. J Hydrol 575:638–655. https://doi.org/10.1016/j.jhydrol.2019.05.070
Gu X, Zhang Q, Vijay PS, Zheng Y (2017) Changes in magnitude and frequency of heavy precipitation across China and its potential links to summer temperature. J Hydrol 547:718–731. https://doi.org/10.1016/j.jhydrol.2017.02.041
Guan J, Mustafa MA et al (2006) Identification of contaminant sources in water distribution systems using simulation – optimization method: case study. J Water Resour Plan Manag 132(4):252–262. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:4(252)
Hu C, Dai L, Yan X, Gong W, Liu X, Wang L (2020) Modified NSGA-III for sensor placement in water distribution system. Inf Sci 509:488–500. https://doi.org/10.1016/j.ins.2018.06.055
Hu C, Li M, Zeng D, Guo S (2018a) A survey on sensor placement for contamination detection in water distribution systems. Wirel Netw 24(2):647–661. https://doi.org/10.1007/s11276-016-1358-0
Hu C, Shu X, Yan X, Zeng D, Gong W, Wang L (2018b) Inline wireless mobile sensors and fog nodes placement for leakage detection in water distribution systems. Software Pract Exper 39:1–16. https://doi.org/10.1002/spe.2631
Hu C, Zhao J, Yan X (2015) A map reduce based parallel niche genetic algorithm for contaminant source identification in water distribution network. Ad Hoc Netw 35:116–126. https://doi.org/10.1016/j.adhoc.2015.07.011
Huang J, Edward AM (2009) Data mining to identify contaminant event locations in water distribution systems. J Water Resour Plan Manag 135(6):466–474. https://doi.org/10.1061/(ASCE)0733-9496(2009)135:6(466)
Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments– a survey. IEEE Trans Evol Comput 9(3):303–317. https://doi.org/10.1109/TEVC.2005.846356
Kuter S, Akyürek Z, Weber GW (2018) Recent contributions to climate change and water resource management by applying novel analytics on satellite data. EWG-ORD 2018 Workshop OR for Sustainable Development: Establishing Policy and Measuring Goal Attainment, Complutense University of Madrid, Spain, July 5-7, 2018. pp. 1-11
Kuter S, Akyürek Z, Weber GW, Gütmen S (2019) Advancing Water-Resource Management: Application of Novel OR-Analytics - Snow classification on Sentinel-2 imagery by MARS. EWG-ORD 2019 Workshop Renewable: Energy, Health & Sustainability. Dublin, Ireland, June, 2019, pp1-10.
Laird CD, Biegler LT, van Bloemen Waanders BG, Bartlett RA (2005) Contamination Source Determination for Water Networks. J Water Resour Plan Manag 131(2):125–134. https://doi.org/10.1061/(ASCE)0733-9496(2005)131:2(125)
Laird CD, Lorenz TB, Bart GV (2006) Mixed-integer approach for obtaining unique solutions in source inversion of water networks. J Water Resour Plan Manag 132(4):242–251. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:4(242)
Li S, Gong W, Yan X, Hu C, Bai D, Wang L (2019) Parameter estimation of photovoltaic models with memetic adaptive differential evolution. Sol Energy 2019(190):465–474. https://doi.org/10.1016/j.solener.2019.08.022
Liu L, Ranjithan SR, Mahinthakumar G (2011) Contamination source identification in water distribution systems using an adaptive dynamic optimization procedure. J Water Resour Plan Manag 137(2):183–192. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000104
Lv M, et al (2010) Notice of retraction investigation on backward tracking of contamination sources in water supply systems-case study. In 2010 The 2nd Conference on Environmental Science and Information Application Technology 484-487. doi:https://doi.org/10.1109/ESIAT.2010.5568348
Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evolut Comput 6:1–24. https://doi.org/10.1016/j.swevo.2012.05.001
Özmen A (2016) Introduction. In: robust optimization of spline models and complex regulatory networks. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-319-30800-5_1
Perelman L, Avi O (2013) Bayesian Networks for Source Intrusion Detection. J Water Resour Plan Manag 139(4):426–432. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000288
Roche R, Yalcinkaya F (2019) Electrospun polyacrylonitrile nanofibrous membranes for point-of-use water and air cleaning. Chemistry Open 8(1):97–103. https://doi.org/10.1002/open.201800267
Rossman LA (2000) Epanet 2 users manual, US environmental protection agency. Water Supply and Water Resources Division , National Risk Management Research Laboratory, Cincinnati, p 45268
Seth A, Klise KA, Siirola JD, Haxton T, Laird CD (2016) Testing Contamination source identification methods for water distribution networks. J Water Resour Plan Manag 142(4):04016001. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000619
Shang F, James GU, Marios MP (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)
Wang C, Zhou S (2017) Contamination source identification based on sequential Bayesian approach for water distribution network with stochastic demands. IISE Trans 49(9):899–910. https://doi.org/10.1080/24725854.2017.1315782
Wagner DE, Roseanna MN, Cody C (2015) Adjoint-based probabilistic source characterization in water-distribution systems with transient flows and imperfect sensors. Water Res Plan Man 141(9):04015003. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000508
Wang F, Li Y, Zhou A, Tang K (2020a) An Estimation of Distribution Algorithm for Mixed-Variable Newsvendor Problems. IEEE Trans Evol Comput 24(3):479–493. https://doi.org/10.1109/TEVC.2019.2932624
Wang F, Li Y, Liao F, Yan H (2020b) An ensemble learning based prediction strategy for dynamic multi-objective optimization. Appl Soft Comput 96:106592. https://doi.org/10.1016/j.asoc.2020.106592
Wang F, Zhang H, Zhou A (2021) A particle swarm optimization algorithm for mixed-variable optimization problems. Swarm Evol Comput 60:100808. https://doi.org/10.1016/j.swevo.2020.100808
Wang H, Harrison KW (2014) Improving Efficiency of the Bayesian Approach to Water Distribution Contaminant Source Characterization with Support Vector Regression. J Water Resour Plan Manag 140(1):3–11. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000323
Wang H, Xin J (2013) Characterization of groundwater contaminant source using Bayesian method. Stoch Env Res Risk A 27(4):867–876. https://doi.org/10.1007/s00477-012-0622-9
Yan X, Gong W, Wu Q (2017a) Contaminant source identification of water distribution networks using cultural algorithm. Concurr Comp-Pract E 29(24):1–11. https://doi.org/10.1002/cpe.4230
Yan X, Hu C, Sheng VS (2020) Data-driven pollution source location algorithm in water quality monitoring sensor networks. Int J Bio-Inspir Com 15(3):171–180. https://doi.org/10.1504/IJBIC.2020.107474
Yan X, Li T, Hu C, Wu Q (2019b) Real-time localization of pollution source for urban water supply network in emergencies. Clust Comput 22:5941–5954. https://doi.org/10.1007/s10586-018-1725-y
Yan X, Sun J, Hu C (2017b) Research on contaminant sources identification of uncertainty water demand using genetic algorithm. Clust Comput 20(2):1007–1016. https://doi.org/10.1007/s10586-017-0787-6
Yan X, Yang K, Hu C, Gong W (2018) Pollution source positioning in a water supply network based on expensive optimization. Desalin Water Treat 110:308–318. https://doi.org/10.5004/dwt.2018.22330
Yan X, Zhao J, Hu C, Zeng D (2019c) Multimodal optimization problem in contamination source determination of water supply networks. Swarm Evol Comput 47:66–71. https://doi.org/10.1016/j.swevo.2017.05.010
Yan X, Zhu Z, Li T (2019a) Pollution source localization in an urban water supply network based on dynamic water demand. Environ Sci Pollut Res 26(18):17901–17910. https://doi.org/10.1007/s11356-017-0516-y
Yang P, Tang K, Yao X (2018) Turning High-dimensional Optimization into Computationally Expensive Optimization. IEEE Trans Evol Comput 22(1):143–156. https://doi.org/10.1109/TEVC.2017.2672689
Yang S, Li C (2010) A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Ttrans Evolut Comput 14(6):959–974. https://doi.org/10.1109/TEVC.2010.2046667
Yerlikaya-Zkurt F, Taylan P (2020) New computational methods for classification problems in the existence of outliers based on conic quadratic optimization. Commun Stat 49(3/4):753–770. https://doi.org/10.1080/03610918.2019.1661477
Zechman EM, Ranjithan SR (2009) Evolutionary Computation-Based Methods for Characterizing Contaminant Sources in a Water Distribution System. J Water Resour Plan Manag 135(5):334–343. https://doi.org/10.1061/(ASCE)0733-9496(2009)135:5(334)
Acknowledgments
We are very thankful for the potential referees for their valuable suggestions to improve the quality of the manuscript.
Funding
This work is supported by the National Natural Science Foundation of China (Granted Nos. U1911205 and 62073300), the Fundamental Research Funds for the Central Universities, CUG (Granted Nos.CUGGC03) and the Fundamental Research Funds for the Central Universities, JLU ( Granted Nos.93K172020K18).
Author information
Authors and Affiliations
Contributions
Conceptualization: Xuesong Yan, Chengyu Hu
Methodology: Xuesong Yan, Bin Wu
Formal analysis and investigation: Zhengchen Zhou
Writing—original draft preparation: Xuesong Yan, Zhengchen Zhou
Writing—review and editing: Bin Wu
Funding acquisition: Chengyu Hu
Resources: Chengyu Hu
Supervision: Chengyu Hu.
Corresponding author
Ethics declarations
Ethical approval and consent to participate
Not Applicable
Consent for publication
On behalf of all authors, I declare that this manuscript is the authors’ original work and has not been published nor has it been submitted simultaneously elsewhere and all presentations of case reports must have consent for publication.
Competing interests
The authors declared that they have no conflicts of interest to this work.
Additional information
Responsible editor: Xianliang Yi
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yan, X., Zhou, Z., Hu, C. et al. Real-time location algorithms of drinking water pollution sources based on domain knowledge. Environ Sci Pollut Res 28, 46266–46280 (2021). https://doi.org/10.1007/s11356-021-13352-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-13352-4