Abstract
Determining the optimal rates of groundwater extraction for the sustainable use of coastal aquifers is a complex water resources management problem. It necessitates the application of a 3D simulation model for coupled flow and transport simulation together with an optimization algorithm in a linked simulation-optimization framework. The use of numerical models for aquifer simulation within optimization models is constrained by the huge computational burden involved. Approximation surrogates are widely used to replace the numerical simulation model, the widely used surrogate model being Artificial Neural Networks (ANN). This study evaluates genetic programming (GP) as a potential surrogate modeling tool and compares the advantages and disadvantages with the neural network based surrogate modeling approach. Two linked simulation optimization models based on ANN and GP surrogate models are developed to determine the optimal groundwater extraction rates for an illustrative coastal aquifer. The surrogate models are linked to a genetic algorithm for optimization. The optimal solutions obtained using the two approaches are compared and the advantages of GP over the ANN surrogates evaluated.
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References
Ahlfeld DP, Heidari M (1994) Applications of optimal hydraulic control to groundwater. J Water Resour Plan Manage-Asce 120(3):350–365
Aly AH, Peralta RC (1999) Optimal design of aquifer cleanup systems under uncertainty using a neural network and a genetic algorithm. Water Resour Res 35(8):2523–2532
Ataie-Ashtiani B, Ketabchi H (2011) Elitist continuous ant colony optimization algorithm for optimal management of coastal aquifers. Water Resour Manag. 25(1):165–190. doi:10.1007/s11269-010-9693-x
Ayvaz MT, Karahan H (2008) A simulation/optimization model for the identification of unknown groundwater well locations and pumping rates. J Hydrol 357(1–2):76–92
Babovic V, Keijzer M (2002) Rainfall runoff modelling based on genetic programming. Nord Hydrol 33(5):331–346
Bhattacharjya RK, Datta B (2009) ANN-GA-based model for multiple objective management of coastal aquifers. J Water Resour Plan Manage-Asce 135(5):314–322
Cheng AHD, Halhal D, Naji A, Ouazar D (2000) Pumping optimization in saltwater-intruded coastal aquifers. Water Resour Res 36(8):2155–2165
Dagan G, Zeitoun DG (1998) Seawater–freshwater interface in a stratified aquifer of random permeability distribution. J Contam Hydrol 29(3):185–203
Das A, Datta B (1999a) Development of management models for sustainable use of coastal aquifers. J Irrig Drain Eng-Asce 125(3):112–121
Das A, Datta B (1999b) Development of multiobjective management models for coastal aquifers. J Water Resour Plan Manage-Asce 125(2):76–87
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley
Dhar A, Datta B (2009) Multi-objective management of saltwater intrusion in coastal aquifers using linked simulation optimization—methodology development and performance evaluation. J Hydrol Eng-Asce 14(12):1263–1272
Dorado J, Rabunal JR, Puertas J, Santos A, Rivero D (2002) In: Cagnoni S, Gottieb J, Hart E, Middendorf M, Raidl GR (eds), Prediction and modelling of the flow of a typical urban basin through genetic programming, pp 190–201
Emch PG, Yeh WWG (1998) Management model for conjunctive use of coastal surface water and ground water. J Water Resour Plan Manage-Asce 124(3):129–139
Ferreira da Silva JF, Haie N (2007) Optimal locations of groundwater extractions in coastal aquifers. Water Resour Manag 21:1299–1311
Francone FD (1998) DiscipulusTM Software Owner’s Manual, version 3.0 DRAFT. Machine Learning Technologies Inc, Littleton, CO, USA
Garson GD (1991) Intelligence neural network connection weights. Artif Intell Expert 6:47–51
Gaur S, Deo MC (2008) Real-time wave forecasting using genetic programming. Ocean Eng 35(11–12):1166–1172
Gevrey M, Dimopoulos L, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 249–264
Gorelick SM (1983) A review of distributed parameter groundwater management modeling methods. Water Resour Res 19(2):305–319
Hallaji K, Yazicigil H (1996) Optimal management of a coastal aquifer in southern Turkey. J Water Resour Plan Manage-Asce 122(4):233–244
Iribar V, Carrera J, Custodio E, Medina A (1997) Inverse modelling of seawater intrusion in the Llobregat delta deep aquifer. J Hydrol 198(1–4):226–244
Katsifarakis KL, Petala Z (2006) Combining genetic algorithms and boundary elements to optimize coastal aquifers’ management. J Hydrol 327(1–2):200–207
Kourakos G, Mantoglou A (2009) Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models. Adv Water Resour 32(4):507–521
Kourakos G, Mantoglou A (2011) Simulation and multi-objective management of coastal aquifers in semi arid regions. Water Resour Manag 25:1063–1074
Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4(2):87–112
Lin H-CJ, Richards DR, Talbot CA, Yeh G-T, Cheng J-R, Cheng H-P, Jones NL (1997) A three-dimensional finite element computer model for simulating density-dependent flow and transport in variable saturated media: version 3.0. US Army Engineer Research and Development Center, Vicksburg, Miss
Makkeasorn A, Chang NB, Zhou X (2008) Short-term streamflow forecasting with global climate change implications—a comparative study between genetic programming and neural network models. J Hydrol 352(3–4):336–354
Mantoglou A (2003) Pumping management of coastal aquifers using analytical models of saltwater intrusion. Water Resour Res 39(12):1335. doi:10.1029/2002WR001891
Mantoglou A, Papantoniou M (2008) Optimal design of pumping networks in coastal aquifers using sharp interface models. J Hydrol 361(1–2):52–63
Mantoglou A, Papantoniou M, Giannoulopoulos P (2004) Management of coastal aquifers based on nonlinear optimization and evolutionary algorithms. J Hydrol 297(1–4):209–228
Parasuraman K, Elshorbagy A (2008) Toward improving the reliability of hydrologic prediction: model structure uncertainty and its quantification using ensemble-based genetic programming framework. Water Resour Res 44:W12406. doi:10.1029/2007WR006451
Park CH, Aral MM (2004) Multi-objective optimization of pumping rates and well placement in coastal aquifers. J Hydrol 290(1–2):80–99
Ranjithan S, Eheart JW, Garrett JH (1993) Neural network based screening for groundwater reclamation under uncertainty. Water Resour Res 29(3):563–574
Rao SVN, Bhallamudi SM, Thandaveswara BS, Mishra GC (2004) Conjunctive use of surface and groundwater for coastal and deltaic systems. J Water Resour Plan Manage-Asce 130(3):255–267
Rogers LL, Dowla FU, Johnson VM (1995) Optimal field-scale groundwater remediation using neural networks and the genetic algorithm. Environ Sci Technol 29(5):1145–1155
Sheta AF, Mahmoud A (2001) Forecasting using genetic programming. In: Proceedings of the 33rd Southeastern Symposium on System Theory, pp 343–347
Wang M, Zheng C (1998) Ground water management optimization using genetic algorithms and simulated annealing: formulation and comparison. J Am Water Resour Assoc 34(3):519–530
Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3–4):294–306
Yan SQ, Minsker B (2006) Optimal groundwater remediation design using an adaptive neural network genetic algorithm. Water Resour Res 42(5):W05407. doi:10.1029/2005WR004303
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Sreekanth, J., Datta, B. Comparative Evaluation of Genetic Programming and Neural Network as Potential Surrogate Models for Coastal Aquifer Management. Water Resour Manage 25, 3201–3218 (2011). https://doi.org/10.1007/s11269-011-9852-8
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DOI: https://doi.org/10.1007/s11269-011-9852-8