We herein propose a simulation-optimization model for groundwater remediation, using PAT (pump and treat), by coupling artificial neural network (ANN) with the grey wolf optimizer (GWO). The input and output datasets to train and validate the ANN model are generated by repetitively simulating the groundwater flow and solute transport processes using the analytic element method (AEM) and random walk particle tracking (RWPT). The input dataset is the different realization of the pumping strategy and output dataset are hydraulic head and contaminant concentration at predefined locations. The ANN model is used to approximate the flow and transport processes of two unconfined aquifer case studies. The performance evaluation of the ANN model showed that the value of mean squared error (MSE) is close to zero and the value of the correlation coefficient (R) is close to 0.99. These results certainly depict high accuracy of the ANN model in approximating the AEM-RWPT model. Further, the ANN model is coupled with the GWO and it is used for remediation design using PAT. A comparison of the results of the ANN-GWO model with solutions of ANN-PSO (ANN-Particle Swarm Optimization) and ANN-DE (ANN-Differential Evolution) models illustrates the better stability and convergence behaviour of the proposed methodology for groundwater remediation.
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Ahlfeld DP, Mulvey JM, Pinder GF, Wood EF (1988) Contaminated groundwater remediation design using simulation, optimization, and sensitivity theory 1. Model development. Water Resour Res 24:431–441. https://doi.org/10.1029/WR024i003p00431
Akbarpour A, Zeynali MJ, Tahroudi MN (2019) Locating optimal position of pumping Wells in aquifer using meta-heuristic algorithms and finite element method
Bear J, Cheng AHD (2010) Modeling groundwater flow and contaminant transport, vol 23. Springer Science & Business Media, Dordrecht
Bear J, Sun Y (1998) Optimization of pump-treat-inject (PTI) design for the remediation of a contaminated aquifer: multi-stage design with chance constraints. J Contam Hydrol 29:225–244. https://doi.org/10.1016/S0169-7722(97)00023-5
Bechtold M, Vanderborght J, Ippisch O, Vereecken H (2011) Efficient random walk particle tracking algorithm for advective-dispersive transport in media with discontinuous dispersion coefficients and water contents. Water Resour Res 47. https://doi.org/10.1029/2010WR010267
Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30:413–435. https://doi.org/10.1007/s00521-017-3272-5
Gaur S, Chahar BR, Graillot D (2011) Analytic elements method and particle swarm optimization based simulation-optimization model for groundwater management. J Hydrol 402:217–227. https://doi.org/10.1016/j.jhydrol.2011.03.016
Gaur S, Ch S, Graillot D et al (2013) Application of artificial neural networks and particle swarm optimization for the Management of Groundwater Resources. Water Resour Manag 27:927–941. https://doi.org/10.1007/s11269-012-0226-7
Gong Y, Zhang Y, Lan S, Wang H (2015) A comparative study of artificial neural networks, support vector machines and adaptive Neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee. Florida Water Resour Manag:375–391. https://doi.org/10.1007/s11269-015-1167-8
Haitjema HM (1995) Analytic element modeling of groundwater flow. Academic press, San Diego
Majumder P, Eldho TI (2016a) A new groundwater management model by coupling analytic element method and reverse particle tracking with cat swarm optimization. Water Resour Manag 30:1953–1972. https://doi.org/10.1007/s11269-016-1262-5
Majumder P, Eldho TI (2016b) Vectorized simulation of groundwater flow and contaminant transport using analytic element method and random walk particle tracking. Hydrol Process 31:1144–1160. https://doi.org/10.1002/hyp.11106
Majumder P, Eldho TI (2019) Reactive contaminant transport simulation using the analytic element method, random walk particle tracking and kernel density estimator. J Contam Hydrol 222:76–88. https://doi.org/10.1016/j.jconhyd.2019.01.006
Mategaonkar M, Eldho TI (2012) Groundwater remediation optimization using a point collocation method and particle swarm optimization. Environ Model Softw 32:37–48. https://doi.org/10.1007/s12046-012-0086-0
Matott LS, Rabideau AJ, Craig JR (2006) Pump-and-treat optimization using analytic element method flow models. Adv Water Resour 29:760–775. https://doi.org/10.1016/j.advwatres.2005.07.009
Matott LS, Leung K, Sim J (2011) Application of MATLAB and Python optimizers to two case studies involving groundwater flow and contaminant transport modeling. Comput Geosci 37:1894–1899. https://doi.org/10.1016/j.cageo.2011.03.017
McKinney DC, Lin M-D (1996) Pump-and-treat ground-water remediation system optimization. J Water Resour Plan Manag 122:128–136
Mirjalili S, Mohammad S, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Patterson J, Gibson A (2017) Deep learning: a practitioner’s approach., 1st edn. O’Reilly Media, Inc, Beijing
Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour Res 30:457–481. https://doi.org/10.1029/93WR01494
Sadeghfam S, Hassanzadeh Y, Khatibi R et al (2019) Groundwater remediation through pump-treat-inject technology using optimum control by artificial intelligence (OCAI). Water Resour Manag:1123–1145. https://doi.org/10.1007/s11269-018-2171-6
Shieh HJ, Peralta RC (2005) Optimal in situ bioremediation design by hybrid genetic algorithm-simulated annealing. J Water Resour Plan Manag 131:67–78. https://doi.org/10.1061/(ASCE)0733-9496(2005)131:1(67)
Song X, Tang L, Zhao S et al (2015) Grey wolf optimizer for parameter estimation in surface waves. Soil Dyn Earthq Eng 75:147–157. https://doi.org/10.1016/j.soildyn.2015.04.004
Strack ODL (1989) Groundwater mechanics. Prentice Hall
Thomas A, Eldho TI, Rastogi AK, Majumder P (2019) A comparative study in aquifer parameter estimation using MFree point collocation method with evolutionary algorithms. J Hydroinf 21:455–473. https://doi.org/10.2166/hydro.2019.105
Wang W, Ahlfeld DP (1994) Optimal groundwater remediation with well location as a decision variable: model development. Water Resour Res 30:1605–1618. https://doi.org/10.1029/93WR03552
Wang JS, Li SX (2019) An improved Grey wolf optimizer based on differential evolution and elimination mechanism. Sci Rep 9:1–21. https://doi.org/10.1038/s41598-019-43546-3
Yan S, Minsker B (2006) Optimal groundwater remediation design using an adaptive neural network genetic algorithm. Water Resour Res 42:1–14. https://doi.org/10.1029/2005WR004303
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• Analytic element method and random walk particle tracking are used for groundwater remediation through an ANN-based approximate model.
• The first-ever use of the Grey wolf optimizer (GWO) for groundwater remediation.
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Majumder, P., Eldho, T. Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation. Water Resour Manage (2020). https://doi.org/10.1007/s11269-019-02472-9
- Groundwater remediation
- Analytic element method (AEM)
- Random walk particle tracking (RWPT)
- Artificial neural network (ANN)
- Grey wolf optimizer GWO)
- Kernel density estimator (KDE)