Advertisement

Water Resources Management

, Volume 28, Issue 12, pp 4161–4182 | Cite as

Linked Simulation-Optimization based Dedicated Monitoring Network Design for Unknown Pollutant Source Identification using Dynamic Time Warping Distance

  • Manish Kumar JhaEmail author
  • Bithin Datta
Article

Abstract

Implementation of monitoring strategy for increasing the efficiency of groundwater pollutant source characterization is often necessary, especially when only inadequate and arbitrary concentration measurement data are initially available. Two main parameters that need to be estimated for efficient and accurate characterization of groundwater pollution sources are: location of the source and the time when the source became active. Complexities involved with the explicit estimation of the time of start and source activity have not been addressed so far in previous studies. The main complexity arises due to the fact that the spatial location and time of activity are inter-related. Therefore, specifying one and solving for the other simplifies the source characterization problem. Hence, in this study, both the source location and time of initiation are treated as unknowns. The developed methodology uses dynamic time warping distance in the linked simulation-optimization model to address some complex issues in designing a monitoring network to efficiently estimate source characteristics including the time of first activity of unknown groundwater source. Performance of the developed methodology is evaluated on illustrative contaminated aquifer. These evaluation results demonstrate the potential use of the developed methodology.

Keywords

Groundwater Dynamic time warping Optimization Inverse problem Linked simulation-optimization Monitoring network design 

References

  1. Aral MM, Guan J, Maslia ML (2001) Identification of contaminant source location and release history in aquifers. J Hydrol Eng 6(3):225–234CrossRefGoogle Scholar
  2. Atmadja J, Bagtzoglou AC (2001) State of the art report on mathematical methods for groundwater pollution source identification. Environ Forensic 2(3):205–214CrossRefGoogle Scholar
  3. Bagtzoglou A, Atmadja J (2005) Mathematical methods for hydrologic inversion: The case of pollution source identification. In: Kassim T (ed) Water pollution, The handbook of environmental chemistry, vol 3. Springer, Berlin, pp 65–96. doi: 10.1007/b11442 Google Scholar
  4. Bagtzoglou AC, Tompson AFB, Dougherty DE (1991) Probabilistic simulation for reliable solute source identification in heterogeneous porous media. In: Ganoulis J (ed) Water resources engineering risk assessment. Springer-Verlag, Heidelberg, pp 189–201CrossRefGoogle Scholar
  5. Bagtzoglou AC, Dougherty DE, Tompson AFB (1992) Application of particle methods to reliable identification of groundwater pollution sources. Water Resour Manag 6:15–23. doi: 10.1007/BF00872184 CrossRefGoogle Scholar
  6. Chadalavada S, Datta B (2008) Dynamic optimal monitoring network design for transient transport of pollutants in groundwater aquifers. Water Resour Manag 22(6):651–670CrossRefGoogle Scholar
  7. Chadalavada S, Datta B, Naidu R (2011a) Optimisation approach for pollution source identification in groundwater: An overview. Int J Environ Waste Manag 8(1):40–61CrossRefGoogle Scholar
  8. Chadalavada S, Datta B, Naidu R (2011b) Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site. Environ Monit Assess 173:929–940. doi: 10.1007/s10661-010-1435-2 CrossRefGoogle Scholar
  9. Datta B (2002) Discussion of “identification of contaminant source location and release history in aquifers. In: Mustafa M, Aral Jiabao Guan, Morris L. Maslia (eds) Journal of hydrologic engineering, vol 7, pp 399–400Google Scholar
  10. Datta B (1992) Optimal design of groundwater quality monitoring network incorporating uncertainties. In: Proceedings of the national symposium on environment, Bhabha atomic research center. Bombay, India, pp 129–131Google Scholar
  11. Datta B, Chakrabarty D, Dhar A (2009a) Optimal dynamic monitoring network design and identification of unknown groundwater pollution sources. Water Resour Manag 23(10):2031–2049CrossRefGoogle Scholar
  12. Datta B, Chakrabarty D, Dhar A (2009b) Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters. J Hydrol 376(1–2):48–57CrossRefGoogle Scholar
  13. Datta B, Prakash O, Campbell S, Escalada G (2013) Efficient identification of unknown groundwater pollution sources using linked simulation-optimization incorporating monitoring location impact factor and frequency factor. Water Resour Manag 27(14):4959–4976CrossRefGoogle Scholar
  14. Datta BDC, Dhar A (2011) Identification of unknown groundwater pollution sources using classical optimization with linked simulation. J Hydro-Environ Res 1:25–36CrossRefGoogle Scholar
  15. Dhar A, Datta B (2010) Logic-based design of groundwater monitoring network for redundancy reduction. J Water Resour Plan Manag 13(1):88–94CrossRefGoogle Scholar
  16. Gorelick SM, Evans B, Remson I (1983) Identifying sources of groundwater pollution: an optimization approach. Water Resour Res 19(3):779–790CrossRefGoogle Scholar
  17. Jha M, Datta B (2013) Three-dimensional groundwater contamination source identification using adaptive simulated annealing. J Hydrol Eng 18(3):307–317. doi: 10.1061/(ASCE)HE.1943-5584.0000624 CrossRefGoogle Scholar
  18. Kollat JB, Reed PM, Maxwel RM (2011) Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics. Water Resour Res 47(W02529). doi: 10.1029/2010WR009194
  19. Loaiciga H, Charbeneau R, Everett L, Fogg G, Hobbs B, Rouhani S (1992) Review of groundwater quality monitoring network design. J Hydraul Eng - ASCE 118(1):11–37CrossRefGoogle Scholar
  20. Mahar PS, Datta B (1997) Optimal monitoring network and ground-water-pollution source identification. J Water Resour Plan Manag 123(4):199–207CrossRefGoogle Scholar
  21. Mahar PS, Datta B (2000) Identification of pollution sources in transient groundwater systems. Water Resour Manag 14(3):209–227CrossRefGoogle Scholar
  22. Mahar PS, Datta B (2001) Optimal identification of ground-water pollution sources and parameter estimation. J Water Resour Plan Manag 127(1):20–29CrossRefGoogle Scholar
  23. Mahinthakumar GK, Sayeed M (2005) Hybrid genetic algorithm – local search methods for solving groundwater source identification inverse problems. J Water Resour Plan Manag 131:45CrossRefGoogle Scholar
  24. Michalak AM, Kitanidis PK (2004) Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling. Water Resour. Manag. 40(8):W08, 302Google Scholar
  25. Minsker B (2003) Long-term groundwater monitoring-the state of the art. American Society of Civil Engineers stock (40678)Google Scholar
  26. Pinder G, Ross J, Dokou Z (2009) Optimal search strategy for the definition of a DNAPL source. Tech rep. Strategic Environmental Research and Development Program (SERDP, US Department of DefenceGoogle Scholar
  27. Rabiner L, Juang BH (1993) Fundamentals of speech recognition, vol 103. Prentice HallGoogle Scholar
  28. Singh RM, Datta B (2004) Groundwater pollution source identification and simultaneous parameter estimation using pattern matching by artificial neural network. Environ Forensic 5(3):143–153CrossRefGoogle Scholar
  29. Singh RM, Datta B (2006) Identification of groundwater pollution sources using GA-based linked simulation optimization model. J Hydrol Eng 11:101CrossRefGoogle Scholar
  30. Singh RM, Datta B (2007) Artificial neural network modeling for identification of unknown pollution sources in groundwater with partially missing concentration observation data. Water Resour Manag 21:557–572. doi: 10.1007/s11269-006-9029-z CrossRefGoogle Scholar
  31. Singh RM, Datta B, Jain A (2004) Identification of unknown groundwater pollution sources using artificial neural networks. J Water Resour Plan Manag 130:506CrossRefGoogle Scholar
  32. Sun AY, Painter SL, Wittmeyer GW (2006a) A constrained robust least squares approach for contaminant release history identification. Water Resour Res 42(4)(W04):414Google Scholar
  33. Sun AY, Painter SL, Wittmeyer GW (2006b) A robust approach for iterative contaminant source location and release history recovery. J Contam Hydrol 88(3-4):181–196CrossRefGoogle Scholar
  34. Sun NZ (1994) Inverse problems in groundwater modeling, pp 12–37Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Engineering and Physical SciencesJames Cook UniversityTownsvilleAustralia
  2. 2.CRC for Contamination Assessment and Remediation of the EnvironmentSalisbury SouthAustralia

Personalised recommendations