Multiobjective Monitoring Network Design for Efficient Identification of Unknown Groundwater Pollution Sources Incorporating Genetic Programming–Based Monitoring
Publication: Journal of Hydrologic Engineering
Volume 19, Issue 11
Abstract
In this study, a multiobjective model for optimal monitoring network design is presented, which aims to improve the accuracy of groundwater pollution source identification using concentration measurement from a designed optimal monitoring network. 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. A selected subset of trained GP models is used to compute the impact factor of pollutant source fluxes at candidate monitoring locations. This impact factor is used as design criteria to find the best monitoring locations. While constraining the maximum number of permissible monitoring locations, the designed monitoring network improves the results of source identification by choosing monitoring locations that reduce the possibility of missing a pollution source. At the same time, the designed monitoring network decreases the degree of nonuniqueness in the set of possible aquifer responses to subjected geochemical stresses. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show the improved efficiency in source identification when concentration measurements from the designed monitoring network are used.
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References
Amirabdollahian, M., and Datta, B. (2013). “Identification of contaminant source characteristics and monitoring network design in groundwater aquifers: An overview.” J. Environ. Prot., 4(5), 26–41.
Bashi-Azghadi, N. S., and Kerachian, R. (2010). “Locating monitoring wells in groundwater systems using embedded optimization and simulation models.” Sci. Total Environ., 408(10), 2189–2198.
Bashi-Azghadi, N. S., Kerachian, R., Bazargan-Lari, R. M., and Solouki, K. (2010). “Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN.” Expert Syst. Appl., 37(10), 7154–7161.
Chandalavada, S., and Datta, B. (2007). “Dynamic optimal monitoring network design for transient transport of pollutants in groundwater aquifers.” Water Resour. Manage., 22(6), 651–670.
Chandalavada, S., Datta, B., and Naidu, R. (2011). “Uncertainty based optimal monitoring network design for chlorinated hydrocarbon contaminated site.” Environ. Monit. Assess., 173(1–4), 929–940.
Cieniawski, S. E., Eheart, W. J., and Ranjithan, S. (1995). “Using genetic algorithm to solve a multiple objective groundwater monitoring problem.” Water Resour. Res., 31(2), 399–409.
Datta, B. (2002). “Discussion of: Identification of contaminant source location and release history in aquifers by Mustafa M. Aral, Jiabao Guan, and Morris L. Maslia.” J. Hydrol. Eng., 399–400.
Datta, B., and Dhiman, D. S. (1996). “Chance-constrained optimal monitoring network design for pollutants in groundwater.” J. Water Resour. Plann. Manage., 180–188.
Dhar, A., and Datta, B. (2007). “Multi-objective design of dynamic monitoring networks for detection of groundwater pollution.” J. Water Resour. Plann. Manage., 329–338.
Dhar, A., and Datta, B. (2010). “Logic-based design of groundwater monitoring network for redundancy reduction.” J. Water Resour. Plann. Manage., 88.
Discipulus 5.1 [Computer software]. RML Technologies, Littleton, CO.
Domenico, P. A., and Schwartz, F. W. (1998). Physical and chemical hydrogeology, 2nd Ed., Wiley, New York.
Feng-guang, Y., Shu-you, C., Xing-nian, L., and Ke-jun, Y. (2008). “Design of groundwater level monitoring network with ordinary kriging.” J. Hydrodyn., 20(3), 339–346.
Fethi, B. J., Loaiciga, A. H., and Marino, A. M. (1994). “Multivariate geostatistical design of groundwater monitoring networks.” J. Water Resour. Plann. Manage., 505–522.
Goffe, W. L. (1996). “SIMANN: A global optimization algorithm using simulated annealing.” Stud. Nonlinear Dyn. Econometrics, 1(3), 1–9.
Grabow, G., Mote, R. C., and Yoder, C. D. (2000). “An empirically-based sequential ground water monitoring network design procedure.” J. Am. Water Resour. Assoc., 36(3), 549–566.
Guo, Y., Wang, F. J., and Yin, L. X. (2011). “Optimizing the groundwater monitoring network using MSN theory.” Procedia Social Behav. Sci., 21, 240–242.
Harbaugh, A. W., Banta, E. R., Hill, M. C., and McDonald, M. G. (2000). “MODFLOW-2000, the U.S. geological survey modular ground-water model.” U.S. Geological Survey Open-File Rep. 00-92, 121.
Hudak, P. F., Loaiciga, A. H., and Marino, A. M. (1995). “Regional-scale ground water quality monitoring via integer programming.” J. Hydrol., 164(1–4), 153–170.
Husam, B. (2010). “Assessment of a groundwater quality monitoring network using vulnerability mapping and geostatistics: A case study from Heretaunga Plains, New Zealand.” Agric. Water Manage., 97(2), 240–246.
Jha, M., and Datta, B. (2012). “Application of simulated annealing in water resources management: Optimal solution of groundwater contamination source characterization problem and monitoring network design problems.” Simulated annealing-single and multiple objective problems, InTech, Rijeka, Croatia, 157–174.
Kirkpatrick, S., Gelatt, D. C., and Vecchi, P. M. (1983). “Optimization by simulated annealing.” Science, 220(4598), 671–680.
Kollat, J. B., Reed, P. M., and Kasprzyk, J. R. (2008). “A new epsilon-dominance hierarchical bayesian optimization algorithm for large multi-objective monitoring network design problems.” Adv. Water Resour., 31(5), 828–845.
Kollat, J. B., Reed, P. M., and Maxwell, R. (2011). “Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics.” Water Resour. Res., 47(2), W02529.
Koza, J. R. (1994). “Genetic programming as a means for programming computers by natural selection.” Stat. Comput., 4(2), 87–112.
Loaiciga, H. A. (1989). “An optimization approach for groundwater quality monitoring network design.” Water Resour. Res., 25(8), 1771–1782.
Loaiciga, H. A., and Hudak, P. F. (1992). “A location modelling approach for groundwater monitoring network augmentation.” Water Resour. Res., 28(3), 643–649.
Loaiciga, H. A., and Hudak, P. F. (1993). “An optimization method for network design in multilayered groundwater flow systems.” Water Resour. Res., 29(8), 2835–2845.
Mahar, P. S., and Datta, B. (1997). “Optimal monitoring network and ground-water-pollution source identification.” J. Water Resour. Plann. Manage., 199–207.
Massmann, J., and Freeze, R. A. (1987). “Groundwater pollution from waste management sites: the interaction between risk-based engineering design and regulatory policy. I: Methodology.” Water Resour. Res., 23(2), 351–367.
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., and Teller, E. (1953). “Equation of state calculations by fast computing machines.” J. Chem. Phys., 21(6), 1087–1092.
Meyer, P. D., and Brill, E. D., Jr. (1988). “A method for locating wells in a groundwater pollution monitoring network under conditions of uncertainty.” Water Resour. Res., 24(8), 1277–1282.
Meyer, P. D., Valocchi, A. J., and Eheart, J. W. (1994). “Monitoring network design to provide initial detection of groundwater pollution.” Water Resour. Res., 30(9), 2647–2659.
Montas, H. J., Mohtar, R. H., Hassan, A. E., and AlKhal, F. A. (2000). “Heuristic space-time design of monitoring wells for pollutant plume characterization in stochastic flow fields.” J. Contam. Hydrol., 43(3–4), 271–301.
Mugunthan, P., and Shoemaker, C. A. (2004). “Time varying optimization for monitoring multiple pollutants under uncertain hydrogeology.” Biorem. J., 8(3–4), 129–146.
Nunes, L. M., Cunha, M. C., and Ribeiro, L. (2004a). “Groundwater monitoring network optimization with redundancy reduction.” J. Water Resour. Plann. Manage., 33–43.
Nunes, L. M., Cunha, M. C., and Ribeiro, L. (2004b). “Optimal space-time coverage and exploration costs in groundwater monitoring networks.” Environ. Monit. Assess., 93(1–3), 103–124.
Prakash, O., and Datta, B. (2013). “Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations.” Environ. Monit. Assess., 185(7), 5611–5626.
Reed, P. M., and Kollat, J. B. (2012). “Save now, pay later? Multi-period many-objective groundwater monitoring design given systematic model errors and uncertainty.” Adv. Water Resour., 35, 55–68.
Reed, P. M., and Minsker, B. S. (2004). “Striking the balance: Long-term groundwater monitoring design for conflicting objective.” J. Water Resour. Plann. Manage., 140–149.
Rushton, K. R., and Redshaw, S. C. (1979). Seepage and groundwater flow, Wiley, New York.
Sreekanth, J., and Datta, B. (2012). “Genetic programming: Efficient modeling tool in hydrology and groundwater management.” New genetic programming- new approaches and successful applications, InTech, Rijeka, Croatia, 227–240.
Sreenivasulu, C., and Datta, B. (2008). “Dynamic optimal monitoring network design for transient transport of pollutants in groundwater aquifers.” Water Resour. Manag., 22(6), 651–670.
Wu, J., Zheng, C., and Chien, C. C. (2005). “Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions.” J. Contam. Hydrol., 77(1–2), 41–65.
Yeh, M. S., Lin, Y. P., and Chang, L. C. (2006). “Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithm.” Environ. Geol., 50(1), 101–121.
Zheng, C., and Wang, P. P. (1999). “MT3DMS, a modular three-dimensional multi-species transport model for simulationof advection, dispersion and chemical reactions of contaminants in groundwater systems.” U.S. Army Engineer Research and Development Center Contract Rep. SERDP-99-1, Vicksburg, MS.
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Published In
Journal of Hydrologic Engineering
Volume 19 • Issue 11 • November 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Apr 3, 2013
Accepted: Dec 3, 2013
Published online: Dec 9, 2013
Published in print: Nov 1, 2014
Discussion open until: Dec 11, 2014
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