Abstract
Seawater intrusion is a common problem in coastal areas. The rational distribution of groundwater exploitation can minimize the scope of seawater intrusion and maximize groundwater exploitation. In this study, an optimization method for the groundwater exploitation layout in coastal areas was proposed. Based on the numerical simulation model of variable-density groundwater, a multiobjective groundwater management model was constructed with the objectives of maximizing groundwater exploitation and minimizing seawater intrusion. The optimization model was solved by nondominated sorted genetic algorithm-II (NSGA-II). To improve the computational efficiency of the optimization model, the surrogate models of the groundwater simulation model were built by using three different methods: kriging, support vector regression (SVR), and kernel extreme learning machines (KELM). Finally, the above methods were tested in Longkou City of China. The results show that the use of surrogate models can greatly reduce the computing time for solving seawater intrusion management problems. The surrogate model of the variable-density groundwater simulation model based on the SVR method has the best performance. The groundwater exploitation layout optimized by the above method is reasonable and can reflect the actual hydrogeological conditions in the study area. This study provides a reliable way to optimize the groundwater exploitation layout in coastal areas.
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References
Abd-Elhamid HF, Javadi AA (2011) A cost-effective method to control seawater intrusion in coastal aquifers. Water Resour Manag 25(11):2755–2780. https://doi.org/10.1007/s11269-011-9837-7
An Y, Lu W, Cheng W (2015) Surrogate model application to the identification of optimal groundwater exploitation scheme based on regression kriging method—a case study of Western Jilin Province. Int J Environ Res Public Health 12(8):8897–8918
An Y, Lu W, Yan X (2018) A surrogate-based simulation–optimization approach application to parameters’ identification for the HydroGeoSphere model. Environ Earth Sci 77(17):621
Ayvaz MT, Elçi A (2018) Identification of the optimum groundwater quality monitoring network using a genetic algorithm based optimization approach. J Hydrol 563:1078–1091. https://doi.org/10.1016/j.jhydrol.2018.06.006
Bau DA, Mayer AS (2006) Stochastic management of pump-and-treat strategies using surrogate functions. Adv Water Resour 29(12):1901–1917
Broad DR, Dandy GC, Maier HR (2005) Water distribution system optimization using metamodels. J Water Resour Plan Manag 131(3):172–180. https://doi.org/10.1061/(ASCE)0733-9496(2005)131:3(172
Broad DR, Maier HR, Dandy GC (2010) Optimal operation of complex water distribution systems using metamodels. J Water Resour Plan Manag 136(4):433–443. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000052
Chen M, Izady A, Abdalla OA (2017) An efficient surrogate-based simulation-optimization method for calibrating a regional MODFLOW model. J Hydrol 544:591–603. https://doi.org/10.1016/j.jhydrol.2016.12.011
Cheng AD, Halhal D, Naji A, Ouazar D (2000) Pumping optimization in saltwater-intruded coastal aquifers. Water Resour Res 36(8):2155–2165
Das A, Datta B (1999) Development of multiobjective management models for coastal aquifers. J Water Resour Plan Manag 125(2):76–87. https://doi.org/10.1061/(ASCE)0733-9496(1999)125:2(76)
Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex systems 9(2):115–148
Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. International conference on parallel problem solving from nature. Springer, Berlin, pp 849–858
Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
Dhar A, Datta B (2009a) Saltwater intrusion management of coastal aquifers. I: linked simulation-optimization. J Hydrol Eng 14(12):1263–1272. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000097
Dhar A, Datta B (2009b) Saltwater intrusion management of coastal aquifers. II: operation uncertainty and monitoring. J Hydrol Eng 14(12):1273–1282. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000155
He L, Huang GH, Lu HW (2009) A coupled simulation-optimization approach for groundwater remediation design under uncertainty: an application to a petroleum-contaminated site. Environ Pollut 157(8–9):2485–2492. https://doi.org/10.1016/j.envpol.2009.03.005
Hou Z, Lu W (2018) Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites. Hydrogeol J 26(3):923–932. https://doi.org/10.1007/s10040-017-1690-1
Hou Z, Dai Z, Lao W, Wang Y, Lu W (2019) Application of mixed-integer nonlinear optimization programming based on ensemble surrogate model for dense nonaqueous phase liquid source identification in groundwater. Environ Eng Sci. https://doi.org/10.1089/ees.2018.0366
Huang GB (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput 6(3):376–390. https://doi.org/10.1007/s12559-014-9255-2
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Jiang X, Lu W, Hou Z, Zhao H, Na J (2015) Ensemble of surrogates-based optimization for identifying an optimal surfactant-enhanced aquifer remediation strategy at heterogeneous DNAPL-contaminated sites. Comput Geosci 84:37–45. https://doi.org/10.1016/j.cageo.2015.08.003
Johnson VM, Rogers LL (2000) Accuracy of neural network approximators in simulation-optimization. J Water Resour Plan Manag 126(2):48–56. https://doi.org/10.1061/(ASCE)0733-9496(2000)126:2(48
Kamali A, Niksokhan MH (2017) Multi-objective optimization for sustainable groundwater management by developing of coupled quantity-quality simulation-optimization model. J Hydroinf 19(6):973–992. https://doi.org/10.2166/hydro.2017.007
Ketabchi H, Ataie-Ashtiani B (2015a) Evolutionary algorithms for the optimal management of coastal groundwater: a comparative study toward future challenges. J Hydrol 520:193–213. https://doi.org/10.1016/j.jhydrol.2014.11.043
Ketabchi H, Ataie-Ashtiani B (2015b) Coastal groundwater optimization—advances, challenges, and practical solutions. Hydrogeol J 23(6):1129–1154
Khu ST, Werner MG (2003) Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling. Hydrol Earth Syst Sci Discuss 7(5):680–692. https://doi.org/10.5194/hess-7-680-2003
Kourakos G, Mantoglou A (2011) Simulation and multi-objective management of coastal aquifers in semi-arid regions. Water Resour Manag 25(4):1063–1074. https://doi.org/10.1007/s11269-010-9677-x
Liu C, Hu Y, Yu T, Xu Q, Liu C, Li X, Shen C (2019) Optimizing the water treatment design and Management of the Artificial Lake with water quality modeling and surrogate-based approach. Water 11(2):391. https://doi.org/10.3390/w11020391
Luo Q, Wu J, Yang Y, Qian J, Wu J (2016) Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty. J Hydrol 534:352–363. https://doi.org/10.1016/j.jhydrol.2016.01.009
Machiwal D, Cloutier V, Güler C, Kazakis N (2018) A review of GIS-integrated statistical techniques for groundwater quality evaluation and protection. Environ Earth Sci 77(19):681–630. https://doi.org/10.1007/s12665-018-7872-x
Mantoglou A (2003) Pumping management of coastal aquifers using analytical models of saltwater intrusion. Water Resour Res 39(12). https://doi.org/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. https://doi.org/10.1016/j.jhydrol.2008.07.022
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. https://doi.org/10.1016/j.jhydrol.2004.04.011
Meyer PD, Brill ED Jr (1988) A method for locating wells in a groundwater monitoring network under conditions of uncertainty. Water Resour Res 24(8):1277–1282. https://doi.org/10.1029/WR024i008p01277
Mugunthan P, Shoemaker CA, Regis RG (2005) Comparison of function approximation, heuristic, and derivative-based methods for automatic calibration of computationally expensive groundwater bioremediation models. Water Resour Res 41(11)
Ouyang Q, Lu W, Hou Z, Zhang Y, Li S, Luo J (2017) Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method. J Contam Hydrol 200:15–23. https://doi.org/10.1016/j.jconhyd.2017.03.004
Park CH, Aral MM (2004) Multi-objective optimization of pumping rates and well placement in coastal aquifers. J Hydrol 290(1–2):80–99. https://doi.org/10.1016/j.jhydrol.2003.11.025
Park N, Shi L (2015) A comprehensive sharp-interface simulation-optimization model for fresh and saline groundwater management in coastal areas. Hydrogeol J 23(6):1195–1204. https://doi.org/10.1007/s10040-015-1268-8
Qahman K, Larabi A, Ouazar D, Naji A, Cheng AHD (2005) Optimal and sustainable extraction of groundwater in coastal aquifers. Stoch Env Res Risk A 19(2):99–110. https://doi.org/10.1007/s00477-004-0218-0
Rao SVN, Thandaveswara BS, Bhallamudi SM, Srinivasulu V (2003) Optimal groundwater management in deltaic regions using simulated annealing and neural networks. Water Resour Manag 17(6):409–428. https://doi.org/10.1023/B:WARM.0000004921.74256.a9
Rao SVN, Bhallamudi SM, Thandaveswara BS, Mishra GC (2004) Conjunctive use of surface and groundwater for coastal and deltaic systems. J Water Resour Plan Manag 130(3):255–267. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:3(255
Razavi S, Tolson BA, Burn DH (2012) Numerical assessment of metamodelling strategies in computationally intensive optimization. Environ Model Softw 34:67–86. https://doi.org/10.1016/j.envsoft.2011.09.010
Reed PM, Hadka D, Herman JD, Kasprzyk JR, Kollat JB (2013) Evolutionary multiobjective optimization in water resources: the past, present, and future. Adv Water Resour 51:438–456. https://doi.org/10.1016/j.advwatres.2012.01.005
Regis RG, Shoemaker CA (2007) A stochastic radial basis function method for the global optimization of expensive functions. INFORMS J Comput 19(4):497–509. https://doi.org/10.1287/ijoc.1060.0182
Reichard EG, Johnson TA (2005) Assessment of regional management strategies for controlling seawater intrusion. J Water Resour Plan Manag 131(4):280–291. https://doi.org/10.1061/(ASCE)0733-9496(2005)131:4(280
Safavi HR, Darzi F, Mariño MA (2010) Simulation-optimization modeling of conjunctive use of surface water and groundwater. Water Resour Manag 24(10):1965–1988. https://doi.org/10.1007/s11269-009-9533-z
Singh A (2014) Optimization modelling for seawater intrusion management. J Hydrol 508:43–52. https://doi.org/10.1016/j.jhydrol.2013.10.042
Singh A (2015) Managing the environmental problem of seawater intrusion in coastal aquifers through simulation–optimization modeling. Ecol Indic 48:498–504
Shi L, Cui L, Park N, Huyakorn PS (2011) Applicability of a sharp-interface model for estimating steady-state salinity at pumping wells—validation against sand tank experiments. J Contam Hydrol 124(1–4):35–42. https://doi.org/10.1016/j.jconhyd.2011.01.005
Song J, Yang Y, Wu J, Wu J, Sun X, Lin J (2018) Adaptive surrogate model based multiobjective optimization for coastal aquifer management. J Hydrol 561:98–111. https://doi.org/10.1016/j.jhydrol.2018.03.063
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(4)
Sreekanth J, Datta B (2015) Simulation-optimization models for the management and monitoring of coastal aquifers. Hydrogeol J 23(6):1155–1166. https://doi.org/10.1007/s10040-015-1272-z
Werner AD, Bakker M, Post VE et al (2013) Seawater intrusion processes, investigation and management: recent advances and future challenges. Adv Water Resour 51:3–26. https://doi.org/10.1016/j.advwatres.2012.03.004
Xing Z, Qu R, Zhao Y, Fu Q, Ji Y, Lu W (2019) Identifying the release history of a groundwater contaminant source based on an ensemble surrogate model. J Hydrol 572:501–516. https://doi.org/10.1016/j.jhydrol.2019.03.020
Yang H, Huang K, Chan L, King I, Lyu MR (2004) Outliers treatment in support vector regression for financial time series prediction. In: international conference on neural information processing. Springer, Berlin, pp 1260–1265
Yun YANG, Jianfeng WU, Xiaomin SUN, Jin LIN, Jichun WU (2012) A hybrid multi-objective evolutionary algorithm for optimal groundwater management under variable density conditions. Acta Geologica Sinica-English Edition 86(1):246–255
Zhang Y, Kimberg DY, Coslett HB, Schwartz MF, Wang Z (2014) Multivariate lesion-symptom mapping using support vector regression. Hum Brain Mapp 35(12):5861–5876
Zhao Y, Lu W, Xiao C (2016) A Kriging surrogate model coupled in simulation–optimization approach for identifying release history of groundwater sources. J Contam Hydrol 185:51–60
Funding
This study was supported by the National Key Research and Development Program of China (No.2016YFC0402800), the National Nature Science Foundation of China (No.41672232), and Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, China.
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Fan, Y., Lu, W., Miao, T. et al. Multiobjective optimization of the groundwater exploitation layout in coastal areas based on multiple surrogate models. Environ Sci Pollut Res 27, 19561–19576 (2020). https://doi.org/10.1007/s11356-020-08367-2
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DOI: https://doi.org/10.1007/s11356-020-08367-2