Water Resources Management

, Volume 27, Issue 14, pp 4959–4976 | Cite as

Efficient Identification of Unknown Groundwater Pollution Sources Using Linked Simulation-Optimization Incorporating Monitoring Location Impact Factor and Frequency Factor

  • Bithin Datta
  • Om PrakashEmail author
  • Sean Campbell
  • Gerry Escalada


This study aims to improve the accuracy of groundwater pollution source identification using concentration measurements from a heuristically designed optimal monitoring network. The designed network is constrained by the maximum number of permissible monitoring locations. The designed monitoring network improves the results of source identification by choosing monitoring locations that reduces the possibility of missing a pollution source, at the same time decreasing the degree of non uniqueness in the set of possible aquifer responses to subjected geo-chemical stresses. The proposed methodology combines the capability of Genetic Programming (GP), and linked simulation-optimization for recreating the flux history of the unknown conservative pollutant sources with limited number of spatiotemporal pollution concentration measurements. The GP models are trained using large number of simulated realizations of the pollutant plumes for varying input flux scenarios. A selected subset of GP models are used to compute the impact factor and frequency factor of pollutant source fluxes, at candidate monitoring locations, which in turn is used to find the best monitoring locations. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show the efficiency in source identification when concentration measurements from the designed monitoring network are utilized.


Optimal monitoring network Groundwater pollution Pollution source identification Genetic programming Simulated annealing Optimization 


  1. Amirabdollahian M, Datta B (2013) Identification of contaminant source characteristics and monitoring network design in groundwater aquifers: an overview. J Environ Prot. doi: 10.4236/jep.2013.45A004 Google Scholar
  2. Bashi-Azghadi NS, Kerachian R (2010) Locating monitoring wells in groundwater systems using embedded optimization and simulation models. Sci Total Environ 408(10):2189–2198Google Scholar
  3. Chandalavada S, Datta B (2007) Dynamic optimal monitoring network design for transient transport of pollutants in groundwater aquifers. Water Resour Manag 22:651–670CrossRefGoogle Scholar
  4. Chandalavada S, Datta B, Naidu R (2011) Uncertainty based optimal monitoring network design for chlorinated hydrocarbon contaminated site. Environ Monit Assess 173:929–940CrossRefGoogle Scholar
  5. Cieniawski SE, Eheart WJ, Ranjithan S (1995) Using genetic algorithm to solve a multiple objective groundwater monitoring problem. Water Resour Res 31(2):399–409CrossRefGoogle Scholar
  6. Datta B, Dhiman DS (1996) Chance-constrained optimal monitoring network design for pollutants in groundwater. J Water Resour Plann Manag 122(3):180–188CrossRefGoogle Scholar
  7. Dhar A, Datta B (2007) Multi-objective design of dynamic monitoring networks for detection of groundwater pollution. J Water Resour Plann Manag 133(4):329–338CrossRefGoogle Scholar
  8. Dhar A, Datta B (2010) Logic-based design of groundwater monitoring network for redundancy reduction. J Water Resour Plann Manag 136:88(2010)CrossRefGoogle Scholar
  9. Domenico PA, Schwartz FW (1998) Physical and chemical hydrogeology, 2nd edn. Wiley, NewYorkGoogle Scholar
  10. Goffe WL (1996) SIMANN: A global optimization algorithm using Simulated Annealing. Studied in Nonlinear Dynamics and. Berkeley Electronic Press, EconometricsGoogle Scholar
  11. Guo Y, Wang FJ, Yin LX (2011) Optimizing the groundwater monitoring network using MSN theory. Procedia Soc Behav Sci 21:240–242CrossRefGoogle Scholar
  12. Harbaugh AW, Banta ER, Hill MC, McDonald MG (2000) MODFLOW-2000, the US Geological Survey modular ground-water model, US Geological Survey Open-File Report 00–92, 121 pGoogle Scholar
  13. Kirkpatrick S, Gelatt DC, Vecchi PM (1983) Optimization by simulated annealing. Science 220:671–680CrossRefGoogle Scholar
  14. Kollat JB, Reed PM, Kasprzyk JR (2008) A new epsilon-dominance hierarchical bayesian optimization algorithm for large multi-objective monitoring network design problems. Adv Water Resour 31(5):828–845CrossRefGoogle Scholar
  15. Kollat JB, Reed PM, Maxwell R (2011) Many-objective groundwater monitoring network design using bias-aware ensemble kalman filtering, evolutionary optimization, and visual analytics. Water Resour Res 47, W02529CrossRefGoogle Scholar
  16. Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Statistics and Computing. doi: 10.1007/BF00175355
  17. Mahar PS, Datta B (1997) Optimal monitoring network and ground-water-pollution source identification. J Water Resour Plann Manag 123(4):199–207CrossRefGoogle Scholar
  18. Montas HJ, Mohtar RH, Hassan AE, AlKhal FA (2000) Heuristic space-time design of monitoring wells for pollutant plume characterization in stochastic flow fields. J Contam Hydrol 43(3–4):271–301CrossRefGoogle Scholar
  19. Mugunthan P, Shoemaker CA (2004) Time varying optimization for monitoring multiple pollutants under uncertain hydrogeology. Bioremed J 8(3–4):129–146CrossRefGoogle Scholar
  20. Nunes LM, Cunha MD, Ribeiro L (2004) Groundwater monitoring network optimization with redundancy reduction. J Water Resour Plann Manag 130(1):33–43CrossRefGoogle Scholar
  21. Prakash O, Datta B (2012) Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations. Environ Monit Assess. doi: 10.1007/s10661-012-2971-8 Google Scholar
  22. Reed PM, Kollat JB (2012) Save now, pay later? Multi-period many-objective groundwater monitoring design given systematic model errors and uncertainty. Adv Water Resour 35:55–68CrossRefGoogle Scholar
  23. Reed PM, Minsker BS (2004) Striking the balance: long-term groundwater monitoring design for conflicting objective. J Water Resour Plann Manag 130(2):140–149CrossRefGoogle Scholar
  24. Rushton KR, Redshaw SC (1979) Seepage and groundwater flow. Wiley, New YorkGoogle Scholar
  25. Sreekanth J, Datta B (2012), Genetic programming: efficient modeling tool in hydrology and groundwater management. In: New genetic programming- new approaches and successful applications. InTech, Rijeka, Croatia, pp. 227–240Google Scholar
  26. Sreekanth J, Datta B (2011) Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization. Water Resour Res 47:1–17CrossRefGoogle Scholar
  27. Sreenivasulu C, Datta B (2008) Dynamic optimal monitoring network design for transient transport of pollutants in groundwater aquifers. Water Resour Manag 22(6):651–670CrossRefGoogle Scholar
  28. Wu J, Zheng C, Chien CC (2005) Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions. J Contam Hydrol 77:41–65CrossRefGoogle Scholar
  29. Yeh MS, Lin YP, Chang LC (2006) Designing an optimal multivariate Geostatistical groundwater quality monitoring network using factorial Kriging and genetic algorithm. J Environ Geol 50:101–121CrossRefGoogle Scholar
  30. Zheng C, Wang PP (1999) MT3DMS, a modular three-dimensional multi-species transport model for simulation of advection, dispersion and chemical reactions of contaminants in groundwater systems. U.S. Army Engineer Research and Development Center Contract Report. SERDP-99-1, Vicksburg, MS, 202 pGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Bithin Datta
    • 1
    • 2
  • Om Prakash
    • 1
    • 2
    Email author
  • Sean Campbell
    • 1
  • Gerry Escalada
    • 1
  1. 1.Discipline of Civil and Environmental Engineering, School of Engineering and Physical SciencesJames Cook UniversityTownsvilleAustralia
  2. 2.CRC for Contamination Assessment and Remediation of the EnvironmentMawson LakesAustralia

Personalised recommendations