Simulation-Optimization Modeling Techniques for Groundwater Management and Sustainability: A Critical Review

Abstract:

Recently, groundwater resources have been subjected to negative impacts from various natural and anthropogenic factors worldwide. Hence, great efforts have been conducted in the literature to find the best management strategies for protection against groundwater quantity shortage and aquifer material contamination. Simulation-optimization (S-O) modeling has been considered one of the most feasible options for groundwater management, providing high accuracy in understanding the complex real-world water issues. This review study aims at clarifying the concepts, techniques, and stepwise methodologies of both simulation models and optimization algorithms, in addition to the applications of the integrated S-O approach in solving groundwater problems. Besides, the linkages between applications of the S-O framework and sustainable development goals (SDGs) from quantity and quality perspectives are illustrated to show the expected beneficial outcomes for various socio-economic and environmental issues in different fields. Based on this review, conclusions are drawn that may be useful for future studies related to groundwater conservation, management, and planning.

• Periodical: Advanced Engineering Forum (Volume 47)
• Pages: 89-100
• DOI: https://doi.org/10.4028/p-50l1j1
• Online since: August 2022
• Authors: Ahmed M. Saqr*, Mona G. Ibrahim, Manabu Fujii, Mahmoud Nasr
• Keywords: Aquifer Material Contamination, Groundwater Management, Groundwater Quantity Shortage, Simulation Optimization, Sustainable Development Goals (SDGs)

References

[1] H. K. Esfahani and B. Datta, Fractal singularity–based multiobjective monitoring networks for reactive species contaminant source characterization,, J. Water Resour. Plan. Manag., vol. 144, no. 6, 2018,.

DOI: 10.1061/(asce)wr.1943-5452.0000880

[2] A. Lal and B. Datta, Optimal groundwater-use strategy for saltwater intrusion management in a Pacific island country,, J. Water Resour. Plan. Manag., vol. 145, no. 9, 2019,.

DOI: 10.1061/(asce)wr.1943-5452.0001090

[3] M. T. Ayvaz and A. Elçi, A groundwater management tool for solving the pumping cost minimization problem for the Tahtali watershed (Izmir-Turkey) using hybrid HS-solver optimization algorithm,, J. Hydrol., vol. 478, p.63–76, 2013,.

DOI: 10.1016/j.jhydrol.2012.11.045

[4] H. Ketabchi and B. Ataie-Ashtiani, Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges,, J. Hydrol., vol. 520, p.193–213, 2015,.

DOI: 10.1016/j.jhydrol.2014.11.043

[5] B. G. Rajeev-Gandhi, R. K. Bhattacharjya, and M. G. Satish, Simulation–optimization-based virus source identification model for 3D unconfined aquifer considering source locations and number as variable,, J. Hazardous, Toxic, Radioact. Waste, vol. 21, no. 2, 2017,.

DOI: 10.1061/(asce)hz.2153-5515.0000334

[6] E. Raei, M. R. Nikoo, and S. Pourshahabi, A multi-objective simulation-optimization model for in situ bioremediation of groundwater contamination: Application of bargaining theory,, J. Hydrol., vol. 551, p.407–422, 2017,.

DOI: 10.1016/j.jhydrol.2017.06.010

[7] S. Ghadimi and H. Ketabchi, Possibility of cooperative management in groundwater resources using an evolutionary hydro-economic simulation-optimization model,, J. Hydrol., vol. 578, 2019,.

DOI: 10.1016/j.jhydrol.2019.124094

[8] G. Y. Ebrahim, A. Jonoski, A. Al-Maktoumi, M. Ahmed, and A. Mynett, Simulation-optimization approach for evaluating the feasibility of managed aquifer recharge in the Samail lower catchment, Oman,, J. Water Resour. Plan. Manag., vol. 142, no. 2, 2016,.

DOI: 10.1061/(asce)wr.1943-5452.0000588

[9] A. M. Saqr, M. G. Ibrahim, M. Fujii, and M. Nasr, Sustainable development goals (SDGs) associated with groundwater over-exploitation vulnerability: Geographic information system-based multi-criteria decision analysis,, Nat. Resour. Res., vol. 30, no. 6, p.4255–4276, 2021,.

DOI: 10.1007/s11053-021-09945-y

[10] R. Peralta, B. Timani, and R. Das, Optimizing safe yield policy implementation,, Water Resour. Manag., vol. 25, no. 2, p.483–508, 2011,.

DOI: 10.1007/s11269-010-9710-0

[11] Q. Ouyang, W. Lu, J. Lin, W. Deng, and W. Cheng, Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites,, J. Contam. Hydrol., vol. 203, p.1–8, 2017,.

DOI: 10.1016/j.jconhyd.2017.05.007

[12] R. Sepahvand, H. R. Safavi, and F. Rezaei, Multi-objective planning for conjunctive use of surface and ground water resources using genetic programming,, Water Resour. Manag., vol. 33, no. 6, p.2123–2137, 2019,.

DOI: 10.1007/s11269-019-02229-4

[13] S. Gaur, A. Dave, A. Gupta, A. Ohri, D. Graillot, and S. B. Dwivedi, Application of artificial neural networks for identifying optimal groundwater pumping and piping network layout,, Water Resour. Manag., vol. 32, no. 15, p.5067–5079, 2018,.

DOI: 10.1007/s11269-018-2128-9

[14] P. Majumder and T. I. Eldho, A new groundwater management model by coupling analytic element method and reverse particle tracking with cat swarm optimization,, Water Resour. Manag., vol. 30, no. 6, p.1953–1972, 2016,.

DOI: 10.1007/s11269-016-1262-5

[15] M. Vali, M. Zare, and S. Razavi, Automatic clustering-based surrogate-assisted genetic algorithm for groundwater remediation system design,, J. Hydrol., 2020,.

DOI: 10.1016/j.jhydrol.2020.125752

[16] H. K. Esfahani and B. Datta, Linked optimal reactive contaminant source characterization in contaminated mine sites: Case study,, J. Water Resour. Plan. Manag., vol. 142, no. 12, 2016,.

DOI: 10.1061/(asce)wr.1943-5452.0000707

[17] A. Singh, Computer-based models for managing the water-resource problems of irrigated agriculture,, Hydrogeol. J., vol. 23, no. 6, p.1217–1227, 2015,.

DOI: 10.1007/s10040-015-1270-1

[18] H. R. Safavi, F. Darzi, and M. A. Mariño, Simulation-optimization modeling of conjunctive use of surface water and groundwater,, Water Resour. Manag., vol. 24, no. 10, p.1965–1988, 2010,.

DOI: 10.1007/s11269-009-9533-z

[19] S. Gaur, B. R. Chahar, and D. Graillot, Analytic elements method and particle swarm optimization based simulation-optimization model for groundwater management,, J. Hydrol., vol. 402, no. 3–4, p.217–227, 2011,.

DOI: 10.1016/j.jhydrol.2011.03.016

[20] L. Meng, Q. Feng, and Q. Li, Coupled simulation–optimization model for draining confined aquifer via underground boreholes to prevent water inrush of coal mines,, Environ. Earth Sci., vol. 77, no. 17, 2018,.

DOI: 10.1007/s12665-018-7794-7

[21] A. Ranjbar and N. Mahjouri, Multi-objective freshwater management in coastal aquifers under uncertainty in hydraulic parameters,, Nat. Resour. Res., vol. 29, no. 4, p.2347–2368, 2020,.

DOI: 10.1007/s11053-019-09585-3

[22] J. Sreekanth and B. Datta, Comparative evaluation of genetic programming and neural network as potential surrogate models for coastal aquifer management,, Water Resour. Manag., vol. 25, no. 13, p.3201–3218, 2011,.

DOI: 10.1007/s11269-011-9852-8

[23] A. W. Harbaugh, MODFLOW-2005 : the U.S. Geological Survey modular ground-water model--the ground-water flow process. Book 6: Modeling techniques, Section A. Ground-water.,, U.S. Geol. Surv. Tech. Methods, (2005).

DOI: 10.3133/tm6a16

[24] H. J. G. Diersch, FEFLOW: Finite element modeling of flow, mass and heat transport in porous and fractured media,, Springer, p.1–996, 2014,.

DOI: 10.1007/978-3-642-38739-5

[25] L. Koskinen, M. Laitinen, J. Lofman, K. Meling, and F. Meszaros, FEFLOW: A finite element code for simulating groundwater flow, heat transfer and solute transport,, Proc. Int. Conf. Dev. Appl. Comput. Tech. to Environ. Stud., p.287–296, (1996).

[26] M. Nasiri, H. K. Moghaddam, and M. Hamidi, Development of multi-criteria decision making methods for reduction of seawater intrusion in coastal aquifers using SEAWAT code,, J. Contam. Hydrol., vol. 242, 2021,.

DOI: 10.1016/j.jconhyd.2021.103848

[27] C. D. Langevin, D. T. Thorne Jr., A. M. Dausman, M. C. Sukop, and W. Guo, SEAWAT version 4: A computer program for simulation of multi-species solute and heat transport,, U.S. Geol. Surv. Tech. Methods B. 6, p.39, (2007).

DOI: 10.3133/tm6a22

[28] S. D. Kim, H. J. Lee, and J. S. Park, Simulation of seawater intrusion range in coastal aquifer using the FEMWATER model for disaster information,, Mar. Georesources Geotechnol., vol. 30, no. 3, p.210–221, 2012,.

DOI: 10.1080/1064119x.2011.602388

[29] H. J. Lin et al., FEMWATER : A three-dimensional finite element computer model for simulating density-dependent flow and transport in variably saturated media,, Tech. Report, US Army Corps Eng., p.1–143, (1997).

[30] F. Heydari, B. Saghafian, and M. Delavar, Coupled quantity-quality simulation-optimization model for conjunctive surface-groundwater use,, Water Resour. Manag., vol. 30, no. 12, p.4381–4397, 2016,.

DOI: 10.1007/s11269-016-1426-3

[31] T. Borah and R. K. Bhattacharjya, Development of an improved pollution source identification model using numerical and ANN based simulation-optimization model,, Water Resour. Manag., vol. 30, no. 14, p.5163–5176, 2016,.

DOI: 10.1007/s11269-016-1476-6

[32] K. Norouzi-Khatiri, M. H. Niksokhan, A. Sarang, and A. Kamali, Coupled simulation-optimization model for the management of groundwater resources by considering uncertainty and conflict resolution,, Water Resour. Manag., vol. 34, no. 11, p.3585–3608, 2020,.

DOI: 10.1007/s11269-020-02637-x

[33] S. Patel, T. I. Eldho, and A. K. Rastogi, Hybrid-metaheuristics based inverse groundwater modelling to estimate hydraulic conductivity in a nonlinear real-field large aquifer system,, Water Resour. Manag., vol. 34, no. 6, p.2011–2028, 2020,.

DOI: 10.1007/s11269-020-02540-5

[34] R. C. Peralta, A. Forghani, and H. Fayad, Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow,, J. Hydrol., vol. 511, p.776–785, 2014,.

DOI: 10.1016/j.jhydrol.2014.01.044

[35] S. Islam and B. Talukdar, A linked simulation–optimization (LSO) model for conjunctive irrigation management using clonal selection algorithm,, J. Inst. Eng. Ser. A, vol. 97, no. 3, p.181–189, 2016,.

DOI: 10.1007/s40030-016-0169-8

[36] V. Christelis and A. Mantoglou, Pumping optimization of coastal aquifers assisted by adaptive metamodelling methods and radial basis functions,, Water Resour. Manag., vol. 30, no. 15, p.5845–5859, 2016,.

DOI: 10.1007/s11269-016-1337-3

[37] A. R. Awad, I. Von Poser, and M. T. Aboul-Ela, Optimal removal of heavy metals pollutants from groundwater using a real genetic algorithm and finite-difference method,, J. Comput. Civ. Eng., vol. 27, no. 5, p.522–533, 2013,.

DOI: 10.1061/(asce)cp.1943-5487.0000147

[38] A. Elçi and M. T. Ayvaz, Differential-evolution algorithm based optimization for the site selection of groundwater production wells with the consideration of the vulnerability concept,, J. Hydrol., vol. 511, p.736–749, 2014,.

DOI: 10.1016/j.jhydrol.2014.01.071

[39] N. S. Kawo, Y. Zhou, R. Magalso, and L. Salvacion, Optimization of an artificial-recharge–pumping system for water supply in the Maghaway valley, Cebu, Philippines,, Hydrogeol. J., vol. 26, no. 3, p.963–977, 2018,.

DOI: 10.1007/s10040-017-1693-y

[40] K. Kumari and A. Dhar, Groundwater management using coupled analytic element based transient groundwater flow and optimization model,, Model. Optim. Sci. Technol., vol. 16, p.119–134, 2020,.

DOI: 10.1007/978-3-030-26458-1_8

[41] H. Lu, J. Li, Y. Chen, and J. Lu, A multi-level method for groundwater remediation management accommodating non-competitive objectives,, J. Hydrol., vol. 570, p.531–543, 2019,.

DOI: 10.1016/j.jhydrol.2019.01.018

[42] A. Dhar and B. Datta, Saltwater intrusion management of coastal aquifers. II: Operation uncertainty and monitoring,, J. Hydrol. Eng., vol. 14, no. 12, p.1273–1282, 2009,.

DOI: 10.1061/(asce)he.1943-5584.0000155

[43] S. N. Bashi-Azghadi and R. Kerachian, Locating monitoring wells in groundwater systems using embedded optimization and simulation models,, Sci. Total Environ., vol. 408, no. 10, p.2189–2198, 2010,.

DOI: 10.1016/j.scitotenv.2010.02.004

[44] J. B. Kollat and P. M. Reed, A computational scaling analysis of multiobjective evolutionary algorithms in long-term groundwater monitoring applications,, Adv. Water Resour., vol. 30, no. 3, p.408–419, 2007,.

DOI: 10.1016/j.advwatres.2006.05.009

[45] J. Sreekanth and B. Datta, Design of an optimal compliance monitoring network and feedback information for adaptive management of saltwater intrusion in coastal aquifers,, J. Water Resour. Plan. Manag., vol. 140, no. 10, 2014,.

DOI: 10.1061/(asce)wr.1943-5452.0000406

[46] A. Sedki and D. Ouazar, Simulation-optimization modeling for sustainable groundwater development: A moroccan coastal aquifer case study,, Water Resour. Manag., vol. 25, no. 11, p.2855–2875, 2011,.

DOI: 10.1007/s11269-011-9843-9

[47] M. K. Jhaa and B. Dattaa, Simulated annealing based simulation-optimization approach for identification of unknown contaminant sources in groundwater aquifers,, Desalin. Water Treat., vol. 32, no. 1–3, p.79–85, 2011,.

DOI: 10.5004/dwt.2011.2681

[48] J. Sreekanth and B. Datta, Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization,, Water Resour. Res., vol. 47, no. 4, 2011,.

DOI: 10.1029/2010wr009683

[49] P. S. Huang and Y. C. Chiu, A simulation-optimization model for seawater intrusion management at Pingtung coastal area, Taiwan,, Water (Switzerland), vol. 10, no. 3, 2018,.

DOI: 10.3390/w10030251

[50] A. Ghaseminejad and M. Shourian, A simulation–optimization approach for optimal design of groundwater withdrawal wells' location and pumping rate considering desalination constraints,, Environ. Earth Sci., vol. 78, no. 9, 2019,.

DOI: 10.1007/s12665-019-8273-5

[51] M. E. Abd Elmaboud, H. A. Abdel-Gawad, K. S. El-Alfy, and M. M. Ezzeldin, Estimation of groundwater recharge using simulation-optimization model and cascade forward ANN at East Nile Delta aquifer, Egypt,, J. Hydrol. Reg. Stud., vol. 34, (2021).

DOI: 10.1016/j.ejrh.2021.100784

[52] A. Zidan, M. Abdalla, S. Khalaf, and A. M. Saqr, Kinetic energy and momentum coefficients for Egyptian irrigation canals,, Mansoura Eng. J., vol. 41, no. 1, p.1–16, 2016,.

DOI: 10.21608/bfemu.2020.99368

[53] A. Zidan, M. Abdalla, S. Khalaf, and A. M. Saqr, Resistance equations for Egyptian irrigation canals (Case study: ʻDakahliya Governorateʼ),, Int. Water Technol. J., vol. 7, no. 2, p.73–90, 2017, doi: https://www.researchgate.net/publication/319035901_Resistance_equations_for_ Egyptian_irrigation_canals_Case_study_Dakahliya_Governorate.

DOI: 10.21608/bfemu.2020.99368

[54] A. Zidan, M. Abdalla, S. Khalaf, and A. M. Saqr, Regime equations for Egyptian irrigation canals (Case study: ʻDakahliya Governorateʼ),, Int. Water Technol. J., vol. 8, no. 4, p.129–141, 2018, doi: https://www.researchgate.net/publication/333131530_Regime_equations_for_Egyptian_irrigation_canals_Case_study_Dakahliya_Governorate.

DOI: 10.21608/bfemu.2020.99368

[55] J. McPhee and W. W. G. Yeh, Experimental design for groundwater modeling and management,, Water Resour. Res., vol. 42, no. 2, 2006,.

DOI: 10.1029/2005wr003997

[56] M. K. Benhachmi, D. Ouazar, A. Naji, A. H. Cheng, and K. E. L. Harrouni, Chance-constrained pumping optimization in saltwater intruded aquifers by simple genetic algorithm — Stochastic model,, p.1–15, (2003).

DOI: 10.1201/9780203493496.ch11

[57] L. Feyen and S. M. Gorelick, Framework to evaluate the worth of hydraulic conductivity data for optimal groundwater resources management in ecologically sensitive areas,, Water Resour. Res., vol. 41, no. 3, p.1–13, 2005,.

DOI: 10.1029/2003wr002901

[58] L. Feyen and S. M. Gorelick, Reliable groundwater management in hydroecologically sensitive areas,, Water Resour. Res., vol. 40, no. 7, 2004,.

DOI: 10.1029/2003wr003003

[59] P. Bayer, C. M. Bürger, and M. Finkel, Computationally efficient stochastic optimization using multiple realizations,, Adv. Water Resour., vol. 31, no. 2, p.399–417, 2008,.

DOI: 10.1016/j.advwatres.2007.09.004

[60] A. Singh and B. S. Minsker, Uncertainty-based multiobjective optimization of groundwater remediation design,, Water Resour. Res., vol. 44, no. 2, 2008,.

DOI: 10.1029/2005wr004436

[61] J. Parker, U. Kim, P. K. Kitanidis, M. Cardiff, and X. Liu, Stochastic cost optimization of multistrategy DNAPL site remediation,, Gr. Water Monit. Remediat., vol. 30, no. 3, p.65–78, 2010,.

DOI: 10.1111/j.1745-6592.2010.01287.x

[62] M. M. Rajabi and B. Ataie-Ashtiani, Sampling efficiency in Monte Carlo based uncertainty propagation strategies: Application in seawater intrusion simulations,, Adv. Water Resour., vol. 67, p.46–64, 2014,.

DOI: 10.1016/j.advwatres.2014.02.004

[63] M. N. Almasri and J. J. Kaluarachchi, Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data,, Environ. Model. Softw., vol. 20, no. 7, p.851–871, 2005,.

DOI: 10.1016/j.envsoft.2004.05.001

[64] G. Corzo and D. Solomatine, Baseflow separation techniques for modular artificial neural network modelling in flow forecasting,, Hydrol. Sci. J., vol. 52, no. 3, p.491–507, 2007,.

DOI: 10.1623/hysj.52.3.491

[65] G. Kourakos and A. Mantoglou, Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models,, Adv. Water Resour., vol. 32, no. 4, p.507–521, 2009,.

DOI: 10.1016/j.advwatres.2009.01.001

[66] G. Kourakos and A. Mantoglou, Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management,, J. Hydrol., vol. 479, p.13–23, 2013,.

DOI: 10.1016/j.jhydrol.2012.10.050

[67] S. V. N. Rao, B. S. Thandaveswara, S. M. Bhallamudi, and V. Srinivasulu, Optimal groundwater management in deltaic regions using simulated annealing and neural networks,, Water Resour. Manag., vol. 17, no. 6, p.409–428, 2003,.

DOI: 10.1023/b:warm.0000004921.74256.a9

[68] S. V. N. Rao, S. M. Bhallamudi, B. S. Thandaveswara, and G. C. Mishra, Conjunctive use of surface and groundwater for coastal and deltaic systems,, J. Water Resour. Plan. Manag., vol. 130, no. 3, p.255–267, 2004,.

DOI: 10.1061/(asce)0733-9496(2004)130:3(255)

[69] R. K. Bhattacharjya, B. Datta, and M. G. Satish, Artificial neural networks approximation of density dependent saltwater intrusion process in coastal aquifers,, J. Hydrol. Eng., vol. 12, no. 3, p.273–282, 2007,.

DOI: 10.1061/(asce)1084-0699(2007)12:3(273)

[70] M. P. Papadopoulou, I. K. Nikolos, and G. P. Karatzas, Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: Saltwater intrusion,, Water Sci. Technol., vol. 62, no. 7, p.1479–1490, 2010,.

DOI: 10.2166/wst.2010.442

[71] R. C. Peralta, A. Forghani, and H. Fayad, Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow,, J. Hydrol., vol. 511, p.776–785, 2014,.

DOI: 10.1016/j.jhydrol.2014.01.044

[72] H. R. Safavi and M. Esmikhani, Conjunctive use of surface water and groundwater: application of support vector machines (SVMs) and genetic algorithms,, Water Resour. Manag., vol. 27, no. 7, p.2623–2644, 2013,.

DOI: 10.1007/s11269-013-0307-2

[73] B. Wu et al., Optimizing water resources management in large river basins with integrated surface water-groundwater modeling: A surrogate-based approach,, Water Resour. Res., vol. 51, no. 4, p.2153–2173, 2015,.

DOI: 10.1002/2014wr016653

[74] V. Christelis and A. Mantoglou, Pumping optimization of coastal aquifers assisted by adaptive metamodelling methods and radial basis functions,, Water Resour. Manag., vol. 30, no. 15, p.5845–5859, 2016,.

DOI: 10.1007/s11269-016-1337-3

[75] V. Christelis, R. G. Regis, and A. Mantoglou, Surrogate-based pumping optimization of coastal aquifers under limited computational budgets,, J. Hydroinformatics, vol. 20, no. 1, p.149–163, 2018,.

DOI: 10.2166/hydro.2017.063

[76] Y. An, W. Lu, and X. Yan, A surrogate-based simulation–optimization approach application to parameters' identification for the hydroGeoSphere model,, Environ. Earth Sci., vol. 77, no. 17, 2018,.

DOI: 10.1007/s12665-018-7806-7

[77] V. Christelis, G. Kopsiaftis, and A. Mantoglou, Performance comparison of multiple and single surrogate models for pumping optimization of coastal aquifers,, Hydrol. Sci. J., vol. 64, no. 3, p.336–349, 2019,.

DOI: 10.1080/02626667.2019.1584400

[78] X. Wu, Y. Zheng, B. Wu, Y. Tian, F. Han, and C. Zheng, Optimizing conjunctive use of surface water and groundwater for irrigation to address human-nature water conflicts: A surrogate modeling approach,, Agric. Water Manag., vol. 163, p.380–392, 2015,.

DOI: 10.1016/j.agwat.2015.08.022

[79] M. Karamouz, R. Kerachian, and B. Zahraie, Monthly water resources and irrigation planning: Case study of conjunctive use of surface and groundwater resources,, J. Irrig. Drain. Eng., vol. 130, no. 5, p.391–402, 2004,.

DOI: 10.1061/(asce)0733-9437(2004)130:5(391)

[80] S. Razavi, B. A. Tolson, and D. H. Burn, Review of surrogate modeling in water resources,, Water Resour. Res., vol. 48, no. 7, 2012,.

DOI: 10.1029/2011wr011527