Multiobjective ensemble surrogate-based optimization algorithm for groundwater optimization designs
•A novel surrogate-based algorithm is proposed for groundwater multiobjective designs.•The adaptive switching technique is proposed to dynamically build the best surrogate.•Three sampling criteria with a population filter improve the ability of the algorithm.•Benchmark and practical cases test the p...
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| Veröffentlicht in: | Journal of hydrology (Amsterdam) Jg. 612; S. 128159 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.09.2022
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| ISSN: | 0022-1694 |
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| Abstract | •A novel surrogate-based algorithm is proposed for groundwater multiobjective designs.•The adaptive switching technique is proposed to dynamically build the best surrogate.•Three sampling criteria with a population filter improve the ability of the algorithm.•Benchmark and practical cases test the performance of the proposed algorithm.
Simulation technique is an increasingly focused method for conveniently evaluating a solution or scenario in the field of groundwater. However, traditional evolutionary algorithms require at least thousands of simulation executions when addressing groundwater simulation-based optimization problems to find reasonable solutions. Intensive simulations usually yield a prohibitive computational burden if the simulation involved is time-consuming. To defeat the issue, this paper proposes a multiobjective ensemble surrogate-based optimization algorithm named MESOA for groundwater optimization designs. Surrogate models can reduce the continual usage of expensive-cost models in a way that approximates objective functions. Unlike existing surrogate-assisted evolutionary algorithms, MESOA employs surrogates with various basis functions (RBFs) and Kriging as available surrogates, and presents an adaptive switching technique to construct surrogate models in an online way. In addition, MESOA involves three sample infill criteria and a novel population filter. With the assistance of these techniques, MESOA can fully depict the outline of the true Pareto front, although the times of invoking simulation are limited. Some representative benchmark cases are provided to test the applicability and effectiveness of the proposed algorithm at first. Afterward, MESOA is applied to solve some practical groundwater multiobjective optimization designs, such as groundwater remediation and requirement optimization. All empirical results indicate that the proposed algorithm obtains more availability and effectiveness than other algorithms and has wide universality for groundwater optimization designs. |
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| AbstractList | •A novel surrogate-based algorithm is proposed for groundwater multiobjective designs.•The adaptive switching technique is proposed to dynamically build the best surrogate.•Three sampling criteria with a population filter improve the ability of the algorithm.•Benchmark and practical cases test the performance of the proposed algorithm.
Simulation technique is an increasingly focused method for conveniently evaluating a solution or scenario in the field of groundwater. However, traditional evolutionary algorithms require at least thousands of simulation executions when addressing groundwater simulation-based optimization problems to find reasonable solutions. Intensive simulations usually yield a prohibitive computational burden if the simulation involved is time-consuming. To defeat the issue, this paper proposes a multiobjective ensemble surrogate-based optimization algorithm named MESOA for groundwater optimization designs. Surrogate models can reduce the continual usage of expensive-cost models in a way that approximates objective functions. Unlike existing surrogate-assisted evolutionary algorithms, MESOA employs surrogates with various basis functions (RBFs) and Kriging as available surrogates, and presents an adaptive switching technique to construct surrogate models in an online way. In addition, MESOA involves three sample infill criteria and a novel population filter. With the assistance of these techniques, MESOA can fully depict the outline of the true Pareto front, although the times of invoking simulation are limited. Some representative benchmark cases are provided to test the applicability and effectiveness of the proposed algorithm at first. Afterward, MESOA is applied to solve some practical groundwater multiobjective optimization designs, such as groundwater remediation and requirement optimization. All empirical results indicate that the proposed algorithm obtains more availability and effectiveness than other algorithms and has wide universality for groundwater optimization designs. Simulation technique is an increasingly focused method for conveniently evaluating a solution or scenario in the field of groundwater. However, traditional evolutionary algorithms require at least thousands of simulation executions when addressing groundwater simulation-based optimization problems to find reasonable solutions. Intensive simulations usually yield a prohibitive computational burden if the simulation involved is time-consuming. To defeat the issue, this paper proposes a multiobjective ensemble surrogate-based optimization algorithm named MESOA for groundwater optimization designs. Surrogate models can reduce the continual usage of expensive-cost models in a way that approximates objective functions. Unlike existing surrogate-assisted evolutionary algorithms, MESOA employs surrogates with various basis functions (RBFs) and Kriging as available surrogates, and presents an adaptive switching technique to construct surrogate models in an online way. In addition, MESOA involves three sample infill criteria and a novel population filter. With the assistance of these techniques, MESOA can fully depict the outline of the true Pareto front, although the times of invoking simulation are limited. Some representative benchmark cases are provided to test the applicability and effectiveness of the proposed algorithm at first. Afterward, MESOA is applied to solve some practical groundwater multiobjective optimization designs, such as groundwater remediation and requirement optimization. All empirical results indicate that the proposed algorithm obtains more availability and effectiveness than other algorithms and has wide universality for groundwater optimization designs. |
| ArticleNumber | 128159 |
| Author | Wang, Zhe Hu, Pengjie Xu, Jin Tang, Hongwu Wang, Lingling Wu, Mengtian |
| Author_xml | – sequence: 1 givenname: Mengtian surname: Wu fullname: Wu, Mengtian organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China – sequence: 2 givenname: Lingling surname: Wang fullname: Wang, Lingling email: wanglingling@hhu.edu.cn organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China – sequence: 3 givenname: Jin surname: Xu fullname: Xu, Jin organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China – sequence: 4 givenname: Zhe surname: Wang fullname: Wang, Zhe organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China – sequence: 5 givenname: Pengjie surname: Hu fullname: Hu, Pengjie organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China – sequence: 6 givenname: Hongwu surname: Tang fullname: Tang, Hongwu organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China |
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| References | Kourakos, G., Mantoglou, A., 2013. Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management. J. Hydrol. 479, 13–23. https://doi.org/10/f4n78c. Zhan, Cheng, Liu (b0255) 2017; 21 Jin (b0150) 2011; 1 Chugh, Jin, Miettinen, Hakanen, Sindhya (b0045) 2016; 22 Gong, Duan, Li, Wang, Di, Dai, Ye, Miao (b0115) 2015; 19 Dong, Li, Wang, Song, Yu (b0105) 2021; 220 Razavi, S., Tolson, B.A., Burn, D.H., 2012a. Review of surrogate modeling in water resources. Water Resour. Res. 48, W07401. https://doi.org/10/gcx7kq. Zitzler, Deb, Thiele (b0280) 2000; 8 Gorelick, Zheng (b0120) 2015; 51 Huntington, Lyrintzis (b0140) 1998; 13 Jones, Schonlau, Welch (b0155) 1998; 13 Zhang, Liu, Tsang, Virginas (b0265) 2009; 14 Bakker, Post, Langevin, Hughes, White, Starn, Fienen (b0015) 2016; 54 Kazemzadeh-Parsi, M.J., Daneshmand, F., Ahmadfard, M.A., Adamowski, J., Martel, R., 2015. Optimal groundwater remediation design of pump and treat systems via a simulation-optimization approach and firefly algorithm. Eng. Optimiz. 47, 1–17. https://doi.org/10/gnp6xv. Deb, Roy, Hussein (b0085) 2021; 26 Chugh, Sindhya, Hakanen, Miettinen (b0050) 2019; 23 Broomhead, Lowe (b0025) 1988 Mo, S., Lu, D., Shi, X., Zhang, G., Ye, M., Wu, Jianfeng, Wu, Jichun, 2017. A taylor expansion‐based adaptive design strategy for global surrogate modeling with applications in groundwater modeling. Water Resour. Res. 53, 10802–10823. https://doi.org/10/gcxxdj. Song, J., Yang, Y., Chen, G., Sun, X., Lin, J., Wu, Jianfeng, Wu, Jichun, 2019. Surrogate assisted multi-objective robust optimization for groundwater monitoring network design. J. Hydrol. 577, 123994. https://doi.org/10/gnp6xk. Karterakis, S.M., Karatzas, G.P., Nikolos, I.K., Papadopoulou, M.P., 2007. Application of linear programming and differential evolutionary optimization methodologies for the solution of coastal subsurface water management problems subject to environmental criteria. J. Hydrol. 342, 270–282. https://doi.org/10/c77kf3. Zheng, C., Wang, P.P., 1999. MT3DMS: a modular three-dimensional multispecies transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems; documentation and user’s guide. Derrac, García, Molina, Herrera (b0095) 2011; 1 Razavi, Tolson, Burn (b0215) 2012; 34 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/gnp6xm. Regis, R.G., Shoemaker, C.A., 2004. Local function approximation in evolutionary algorithms for the optimization of costly functions. IEEE Trans. Evol. Computat. 8, 490–505. https://doi.org/10/bfdcz9. Delshad, Pope, Sepehrnoori (b0090) 1996; 23 Tian, Cheng, Zhang, Jin (b0235) 2017; 12 Zitzler, Thiele (b0285) 1999; 3 Majumder, P., Eldho, T.I., 2020. Artificial neural network and grey wolf optimizer based surrogate simulation-optimization model for groundwater remediation. Water Resour. Manage. 34, 763–783. https://doi.org/10/gjftmv. Li, Cai, Gao, Shen (b0185) 2020; 51 Zhao, Qu, Xing, Lu (b0270) 2020; 138 Li, J., Lu, W., Luo, J., 2021. Groundwater contamination sources identification based on the Long-Short Term Memory network. J. Hydrol. 601, 126670. https://doi.org/10/gm3k3k. Zhang, Wang, Zhu, Zeng (b0260) 2021; 94 Ayvaz, Elci (b0010) 2018; 563 Knowles (b0170) 2006; 10 Coello, Sierra (b0060) 2004 Trefry, Muffels (b0240) 2007; 45 Deb, Hussein, Roy, Toscano-Pulido (b0075) 2018; 23 Elci, A., Ayvaz, M.T., 2014. Differential-Evolution algorithm based optimization for the site selection of groundwater production wells with the consideration of the vulnerability concept. J. Hydrol. 511, 736–749. https://doi.org/10/f52kj6. Yeh, W.W.-G., 2015. Review: Optimization methods for groundwater modeling and management. Hydrogeol. J. 23, 1051–1065. https://doi.org/10/f7pchn. Harbaugh, A.W., 2005. MODFLOW-2005, the US Geological Survey modular ground-water model: the ground-water flow process. US Department of the Interior, US Geological Survey Reston, VA. Deb, Jain (b0080) 2013; 18 Chen, Izady, Abdalla (b0030) 2017; 544 Hughes, J.D., Langevin, C.D., Banta, E.R., 2017. Documentation for the MODFLOW 6 framework (USGS Numbered Series No. 6-A57), Documentation for the MODFLOW 6 framework, Techniques and Methods. U.S. Geological Survey, Reston, VA. https://doi.org/10.3133/tm6A57. Jiang, X., Na, J., 2020. Online surrogate multiobjective optimization algorithm for contaminated groundwater remediation designs. Appl. Math. Modell. 78, 519–538. https://doi.org/10/gnp6xh. Datta, Chakrabarty, Dhar (b0065) 2009; 376 Mitchell (b0200) 1997; 45 Bas, Boyaci (b0020) 2007; 78 Singh (b0225) 2015; 23 Asher, Croke, Jakeman, Peeters (b0005) 2015; 51 Guo, J., Lu, W., Yang, Q., Miao, T., 2019. The application of 0–1 mixed integer nonlinear programming optimization model based on a surrogate model to identify the groundwater pollution source. J. Contam. Hydrol. 220, 18–25. https://doi.org/10/gnp6xj. Chen, Liu, Huang, Chen, Hou, Zhou (b0035) 2021; 598 Leube, P.C., Nowak, W., Schneider, G., 2012. Temporal moments revisited: Why there is no better way for physically based model reduction in time. Water Resour. Res. 48, W11527. https://doi.org/10/gnp6xz. Cheng, Jin, Olhofer, Sendhoff (b0040) 2016; 20 Doherty, Christensen (b0100) 2011; 47 Deb, Pratap, Agarwal, Meyarivan (b0070) 2002; 6 Clarke, S.M., Griebsch, J.H., Simpson, T.W., 2005. Analysis of support vector regression for approximation of complex engineering analyses. https://doi.org/10.1115/1.1897403. Bas (10.1016/j.jhydrol.2022.128159_b0020) 2007; 78 Zhang (10.1016/j.jhydrol.2022.128159_b0260) 2021; 94 Jin (10.1016/j.jhydrol.2022.128159_b0150) 2011; 1 10.1016/j.jhydrol.2022.128159_b0205 Bakker (10.1016/j.jhydrol.2022.128159_b0015) 2016; 54 Dong (10.1016/j.jhydrol.2022.128159_b0105) 2021; 220 10.1016/j.jhydrol.2022.128159_b0165 Trefry (10.1016/j.jhydrol.2022.128159_b0240) 2007; 45 Singh (10.1016/j.jhydrol.2022.128159_b0225) 2015; 23 10.1016/j.jhydrol.2022.128159_b0245 10.1016/j.jhydrol.2022.128159_b0125 Coello (10.1016/j.jhydrol.2022.128159_b0060) 2004 Cheng (10.1016/j.jhydrol.2022.128159_b0040) 2016; 20 Zhang (10.1016/j.jhydrol.2022.128159_b0265) 2009; 14 10.1016/j.jhydrol.2022.128159_b0160 Derrac (10.1016/j.jhydrol.2022.128159_b0095) 2011; 1 Chugh (10.1016/j.jhydrol.2022.128159_b0045) 2016; 22 Doherty (10.1016/j.jhydrol.2022.128159_b0100) 2011; 47 Broomhead (10.1016/j.jhydrol.2022.128159_b0025) 1988 Deb (10.1016/j.jhydrol.2022.128159_b0075) 2018; 23 Delshad (10.1016/j.jhydrol.2022.128159_b0090) 1996; 23 Chen (10.1016/j.jhydrol.2022.128159_b0035) 2021; 598 Zhan (10.1016/j.jhydrol.2022.128159_b0255) 2017; 21 10.1016/j.jhydrol.2022.128159_b0230 10.1016/j.jhydrol.2022.128159_b0110 10.1016/j.jhydrol.2022.128159_b0275 Zhao (10.1016/j.jhydrol.2022.128159_b0270) 2020; 138 Chugh (10.1016/j.jhydrol.2022.128159_b0050) 2019; 23 Knowles (10.1016/j.jhydrol.2022.128159_b0170) 2006; 10 10.1016/j.jhydrol.2022.128159_b0190 10.1016/j.jhydrol.2022.128159_b0195 Li (10.1016/j.jhydrol.2022.128159_b0185) 2020; 51 Zitzler (10.1016/j.jhydrol.2022.128159_b0280) 2000; 8 Jones (10.1016/j.jhydrol.2022.128159_b0155) 1998; 13 Ayvaz (10.1016/j.jhydrol.2022.128159_b0010) 2018; 563 Chen (10.1016/j.jhydrol.2022.128159_b0030) 2017; 544 Huntington (10.1016/j.jhydrol.2022.128159_b0140) 1998; 13 10.1016/j.jhydrol.2022.128159_b0145 Deb (10.1016/j.jhydrol.2022.128159_b0085) 2021; 26 10.1016/j.jhydrol.2022.128159_b0220 10.1016/j.jhydrol.2022.128159_b0180 Deb (10.1016/j.jhydrol.2022.128159_b0080) 2013; 18 Mitchell (10.1016/j.jhydrol.2022.128159_b0200) 1997; 45 Razavi (10.1016/j.jhydrol.2022.128159_b0215) 2012; 34 Gorelick (10.1016/j.jhydrol.2022.128159_b0120) 2015; 51 10.1016/j.jhydrol.2022.128159_b0210 10.1016/j.jhydrol.2022.128159_b0175 10.1016/j.jhydrol.2022.128159_b0055 10.1016/j.jhydrol.2022.128159_b0135 Gong (10.1016/j.jhydrol.2022.128159_b0115) 2015; 19 Deb (10.1016/j.jhydrol.2022.128159_b0070) 2002; 6 Zitzler (10.1016/j.jhydrol.2022.128159_b0285) 1999; 3 10.1016/j.jhydrol.2022.128159_b0250 10.1016/j.jhydrol.2022.128159_b0130 Datta (10.1016/j.jhydrol.2022.128159_b0065) 2009; 376 Tian (10.1016/j.jhydrol.2022.128159_b0235) 2017; 12 Asher (10.1016/j.jhydrol.2022.128159_b0005) 2015; 51 |
| References_xml | – volume: 78 start-page: 836 year: 2007 end-page: 845 ident: b0020 article-title: Modeling and optimization I: Usability of response surface methodology publication-title: J. Food Eng. – reference: Elci, A., Ayvaz, M.T., 2014. Differential-Evolution algorithm based optimization for the site selection of groundwater production wells with the consideration of the vulnerability concept. J. Hydrol. 511, 736–749. https://doi.org/10/f52kj6. – volume: 54 start-page: 733 year: 2016 end-page: 739 ident: b0015 article-title: Scripting MODFLOW model development using python and FloPy publication-title: Groundwater – volume: 13 start-page: 245 year: 1998 end-page: 253 ident: b0140 article-title: Improvements to and limitations of Latin hypercube sampling publication-title: Probab. Eng. Eng. Mech. – year: 1988 ident: b0025 article-title: Radial basis functions, multi-variable functional interpolation and adaptive networks publication-title: Royal Signals and Radar Establishment Malvern (United Kingdom) – reference: Li, J., Lu, W., Luo, J., 2021. Groundwater contamination sources identification based on the Long-Short Term Memory network. J. Hydrol. 601, 126670. https://doi.org/10/gm3k3k. – volume: 376 start-page: 48 year: 2009 end-page: 57 ident: b0065 article-title: Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters publication-title: J. Hydrol. – volume: 94 start-page: 656 year: 2021 end-page: 675 ident: b0260 article-title: Pore-scale simulation of salt fingers in porous media using a coupled iterative source-correction immersed boundary-lattice Boltzmann solver publication-title: Appl. Math. Model. – reference: Jiang, X., Na, J., 2020. Online surrogate multiobjective optimization algorithm for contaminated groundwater remediation designs. Appl. Math. Modell. 78, 519–538. https://doi.org/10/gnp6xh. – volume: 21 start-page: 956 year: 2017 end-page: 975 ident: b0255 article-title: Expected improvement matrix-based infill criteria for expensive multiobjective optimization publication-title: IEEE Trans. Evol. Comput. – reference: Song, J., Yang, Y., Chen, G., Sun, X., Lin, J., Wu, Jianfeng, Wu, Jichun, 2019. Surrogate assisted multi-objective robust optimization for groundwater monitoring network design. J. Hydrol. 577, 123994. https://doi.org/10/gnp6xk. – volume: 51 start-page: 1390 year: 2020 end-page: 1402 ident: b0185 article-title: A surrogate-assisted multiswarm optimization algorithm for high-dimensional computationally expensive problems publication-title: IEEE Trans. Cybern. – reference: Regis, R.G., Shoemaker, C.A., 2004. Local function approximation in evolutionary algorithms for the optimization of costly functions. IEEE Trans. Evol. Computat. 8, 490–505. https://doi.org/10/bfdcz9. – reference: Majumder, P., Eldho, T.I., 2020. Artificial neural network and grey wolf optimizer based surrogate simulation-optimization model for groundwater remediation. Water Resour. Manage. 34, 763–783. https://doi.org/10/gjftmv. – reference: Kourakos, G., Mantoglou, A., 2013. Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management. J. Hydrol. 479, 13–23. https://doi.org/10/f4n78c. – volume: 14 start-page: 456 year: 2009 end-page: 474 ident: b0265 article-title: Expensive multiobjective optimization by MOEA/D with Gaussian process model publication-title: IEEE Trans. Evol. Comput. – volume: 220 year: 2021 ident: b0105 article-title: Surrogate-guided multi-objective optimization (SGMOO) using an efficient online sampling strategy publication-title: Knowl. Based. Syst. – volume: 138 year: 2020 ident: b0270 article-title: Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms publication-title: Adv. Water Resour. – volume: 51 start-page: 5957 year: 2015 end-page: 5973 ident: b0005 article-title: A review of surrogate models and their application to groundwater modeling: surrogates of groundwater models publication-title: Water Resour. Res. – volume: 47 start-page: W12534 year: 2011 ident: b0100 article-title: Use of paired simple and complex models to reduce predictive bias and quantify uncertainty publication-title: Water Resour. Res. – volume: 563 start-page: 1078 year: 2018 end-page: 1091 ident: b0010 article-title: Identification of the optimum groundwater quality monitoring network using a genetic algorithm based optimization approach publication-title: J. Hydrol. – volume: 22 start-page: 129 year: 2016 end-page: 142 ident: b0045 article-title: A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization publication-title: IEEE Trans. Evol. Comput. – reference: Mo, S., Lu, D., Shi, X., Zhang, G., Ye, M., Wu, Jianfeng, Wu, Jichun, 2017. A taylor expansion‐based adaptive design strategy for global surrogate modeling with applications in groundwater modeling. Water Resour. Res. 53, 10802–10823. https://doi.org/10/gcxxdj. – volume: 8 start-page: 173 year: 2000 end-page: 195 ident: b0280 article-title: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results publication-title: Evol. Comput. – volume: 1 start-page: 61 year: 2011 end-page: 70 ident: b0150 article-title: Surrogate-assisted evolutionary computation: Recent advances and future challenges publication-title: Swarm Evol. Comput. – reference: Guo, J., Lu, W., Yang, Q., Miao, T., 2019. The application of 0–1 mixed integer nonlinear programming optimization model based on a surrogate model to identify the groundwater pollution source. J. Contam. Hydrol. 220, 18–25. https://doi.org/10/gnp6xj. – volume: 20 start-page: 773 year: 2016 end-page: 791 ident: b0040 article-title: A reference vector guided evolutionary algorithm for many-objective optimization publication-title: IEEE Trans. Evol. Comput. – volume: 19 start-page: 2409 year: 2015 end-page: 2425 ident: b0115 article-title: Multi-objective parameter optimization of common land model using adaptive surrogate modeling publication-title: Hydrol. Earth Syst. Sci. – reference: Hughes, J.D., Langevin, C.D., Banta, E.R., 2017. Documentation for the MODFLOW 6 framework (USGS Numbered Series No. 6-A57), Documentation for the MODFLOW 6 framework, Techniques and Methods. U.S. Geological Survey, Reston, VA. https://doi.org/10.3133/tm6A57. – volume: 544 start-page: 591 year: 2017 end-page: 603 ident: b0030 article-title: An efficient surrogate-based simulation-optimization method for calibrating a regional MODFLOW model publication-title: J. Hydrol. – reference: Kazemzadeh-Parsi, M.J., Daneshmand, F., Ahmadfard, M.A., Adamowski, J., Martel, R., 2015. Optimal groundwater remediation design of pump and treat systems via a simulation-optimization approach and firefly algorithm. Eng. Optimiz. 47, 1–17. https://doi.org/10/gnp6xv. – reference: Clarke, S.M., Griebsch, J.H., Simpson, T.W., 2005. Analysis of support vector regression for approximation of complex engineering analyses. https://doi.org/10.1115/1.1897403. – reference: Harbaugh, A.W., 2005. MODFLOW-2005, the US Geological Survey modular ground-water model: the ground-water flow process. US Department of the Interior, US Geological Survey Reston, VA. – volume: 26 start-page: 5 year: 2021 ident: b0085 article-title: Surrogate modeling approaches for multiobjective optimization: methods, taxonomy, and results publication-title: Mathem. Comput. Appl. – volume: 45 start-page: 81 year: 1997 end-page: 127 ident: b0200 article-title: Artificial neural networks publication-title: Machine Learn. – volume: 598 year: 2021 ident: b0035 article-title: Development of a surrogate method of groundwater modeling using gated recurrent unit to improve the efficiency of parameter auto-calibration and global sensitivity analysis publication-title: J. Hydrol. – volume: 12 start-page: 73 year: 2017 end-page: 87 ident: b0235 article-title: PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum] publication-title: IEEE Comput. Intell. Mag. – volume: 45 start-page: 525 year: 2007 end-page: 528 ident: b0240 article-title: FEFLOW: A finite-element ground water flow and transport modeling tool publication-title: Groundwater – reference: Razavi, S., Tolson, B.A., Burn, D.H., 2012a. Review of surrogate modeling in water resources. Water Resour. Res. 48, W07401. https://doi.org/10/gcx7kq. – volume: 23 start-page: 104 year: 2018 end-page: 116 ident: b0075 article-title: A taxonomy for metamodeling frameworks for evolutionary multiobjective optimization publication-title: IEEE Trans. Evol. Comput. – reference: 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/gnp6xm. – volume: 34 start-page: 67 year: 2012 end-page: 86 ident: b0215 article-title: Numerical assessment of metamodelling strategies in computationally intensive optimization publication-title: Environ. Modell. Softw. – reference: Zheng, C., Wang, P.P., 1999. MT3DMS: a modular three-dimensional multispecies transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems; documentation and user’s guide. – volume: 23 start-page: 303 year: 1996 end-page: 327 ident: b0090 article-title: A compositional simulator for modeling surfactant enhanced aquifer remediation.1 publication-title: Formulation. J. Contam. Hydrol. – volume: 51 start-page: 3031 year: 2015 end-page: 3051 ident: b0120 article-title: Global change and the groundwater management challenge publication-title: Water Resour. Res. – volume: 18 start-page: 577 year: 2013 end-page: 601 ident: b0080 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints publication-title: IEEE Trans. Evolut. Comput. – reference: Yeh, W.W.-G., 2015. Review: Optimization methods for groundwater modeling and management. Hydrogeol. J. 23, 1051–1065. https://doi.org/10/f7pchn. – volume: 23 start-page: 1217 year: 2015 end-page: 1227 ident: b0225 article-title: Review: Computer-based models for managing the water-resource problems of irrigated agriculture publication-title: Hydrogeol. J. – volume: 13 start-page: 455 year: 1998 end-page: 492 ident: b0155 article-title: Efficient global optimization of expensive black-box functions publication-title: J. Global Optim. – volume: 10 start-page: 50 year: 2006 end-page: 66 ident: b0170 article-title: ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems publication-title: IEEE Trans. Evolut. Comp. – reference: Leube, P.C., Nowak, W., Schneider, G., 2012. Temporal moments revisited: Why there is no better way for physically based model reduction in time. Water Resour. Res. 48, W11527. https://doi.org/10/gnp6xz. – volume: 1 start-page: 3 year: 2011 end-page: 18 ident: b0095 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm Evol. Comput. – volume: 23 start-page: 3137 year: 2019 end-page: 3166 ident: b0050 article-title: A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms publication-title: Soft Computing – volume: 3 start-page: 257 year: 1999 end-page: 271 ident: b0285 article-title: Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach publication-title: IEEE Trans. Evol. Comput. – start-page: 688 year: 2004 end-page: 697 ident: b0060 article-title: A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm publication-title: Micai 2004: Advances in Artificial Intelligence – reference: Karterakis, S.M., Karatzas, G.P., Nikolos, I.K., Papadopoulou, M.P., 2007. Application of linear programming and differential evolutionary optimization methodologies for the solution of coastal subsurface water management problems subject to environmental criteria. J. Hydrol. 342, 270–282. https://doi.org/10/c77kf3. – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: b0070 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. – volume: 18 start-page: 577 year: 2013 ident: 10.1016/j.jhydrol.2022.128159_b0080 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints publication-title: IEEE Trans. Evolut. Comput. doi: 10.1109/TEVC.2013.2281535 – ident: 10.1016/j.jhydrol.2022.128159_b0165 doi: 10.1080/0305215X.2013.858138 – volume: 1 start-page: 3 year: 2011 ident: 10.1016/j.jhydrol.2022.128159_b0095 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2011.02.002 – ident: 10.1016/j.jhydrol.2022.128159_b0145 doi: 10.1016/j.apm.2019.09.053 – volume: 376 start-page: 48 year: 2009 ident: 10.1016/j.jhydrol.2022.128159_b0065 article-title: Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2009.07.014 – volume: 19 start-page: 2409 year: 2015 ident: 10.1016/j.jhydrol.2022.128159_b0115 article-title: Multi-objective parameter optimization of common land model using adaptive surrogate modeling publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-19-2409-2015 – volume: 21 start-page: 956 year: 2017 ident: 10.1016/j.jhydrol.2022.128159_b0255 article-title: Expected improvement matrix-based infill criteria for expensive multiobjective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2017.2697503 – year: 1988 ident: 10.1016/j.jhydrol.2022.128159_b0025 article-title: Radial basis functions, multi-variable functional interpolation and adaptive networks – start-page: 688 year: 2004 ident: 10.1016/j.jhydrol.2022.128159_b0060 article-title: A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm – volume: 563 start-page: 1078 year: 2018 ident: 10.1016/j.jhydrol.2022.128159_b0010 article-title: Identification of the optimum groundwater quality monitoring network using a genetic algorithm based optimization approach publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.06.006 – volume: 23 start-page: 303 year: 1996 ident: 10.1016/j.jhydrol.2022.128159_b0090 article-title: A compositional simulator for modeling surfactant enhanced aquifer remediation.1 publication-title: Formulation. J. Contam. Hydrol. doi: 10.1016/0169-7722(95)00106-9 – ident: 10.1016/j.jhydrol.2022.128159_b0210 doi: 10.1029/2011WR011527 – volume: 54 start-page: 733 year: 2016 ident: 10.1016/j.jhydrol.2022.128159_b0015 article-title: Scripting MODFLOW model development using python and FloPy publication-title: Groundwater doi: 10.1111/gwat.12413 – ident: 10.1016/j.jhydrol.2022.128159_b0230 doi: 10.1016/j.jhydrol.2019.123994 – volume: 47 start-page: W12534 year: 2011 ident: 10.1016/j.jhydrol.2022.128159_b0100 article-title: Use of paired simple and complex models to reduce predictive bias and quantify uncertainty publication-title: Water Resour. Res. doi: 10.1029/2011WR010763 – ident: 10.1016/j.jhydrol.2022.128159_b0110 doi: 10.1016/j.jhydrol.2014.01.071 – volume: 45 start-page: 525 year: 2007 ident: 10.1016/j.jhydrol.2022.128159_b0240 article-title: FEFLOW: A finite-element ground water flow and transport modeling tool publication-title: Groundwater doi: 10.1111/j.1745-6584.2007.00358.x – volume: 34 start-page: 67 year: 2012 ident: 10.1016/j.jhydrol.2022.128159_b0215 article-title: Numerical assessment of metamodelling strategies in computationally intensive optimization publication-title: Environ. Modell. Softw. doi: 10.1016/j.envsoft.2011.09.010 – ident: 10.1016/j.jhydrol.2022.128159_b0125 doi: 10.1016/j.jconhyd.2018.11.005 – ident: 10.1016/j.jhydrol.2022.128159_b0055 doi: 10.1115/1.1897403 – ident: 10.1016/j.jhydrol.2022.128159_b0135 doi: 10.3133/tm6A57 – ident: 10.1016/j.jhydrol.2022.128159_b0245 doi: 10.1016/j.jhydrol.2019.03.020 – volume: 13 start-page: 455 year: 1998 ident: 10.1016/j.jhydrol.2022.128159_b0155 article-title: Efficient global optimization of expensive black-box functions publication-title: J. Global Optim. doi: 10.1023/A:1008306431147 – volume: 138 year: 2020 ident: 10.1016/j.jhydrol.2022.128159_b0270 article-title: Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms publication-title: Adv. Water Resour. doi: 10.1016/j.advwatres.2020.103540 – volume: 23 start-page: 1217 year: 2015 ident: 10.1016/j.jhydrol.2022.128159_b0225 article-title: Review: Computer-based models for managing the water-resource problems of irrigated agriculture publication-title: Hydrogeol. J. doi: 10.1007/s10040-015-1270-1 – volume: 51 start-page: 3031 year: 2015 ident: 10.1016/j.jhydrol.2022.128159_b0120 article-title: Global change and the groundwater management challenge publication-title: Water Resour. Res. doi: 10.1002/2014WR016825 – ident: 10.1016/j.jhydrol.2022.128159_b0275 – ident: 10.1016/j.jhydrol.2022.128159_b0205 doi: 10.1002/2017WR021622 – volume: 51 start-page: 1390 year: 2020 ident: 10.1016/j.jhydrol.2022.128159_b0185 article-title: A surrogate-assisted multiswarm optimization algorithm for high-dimensional computationally expensive problems publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2020.2967553 – volume: 22 start-page: 129 year: 2016 ident: 10.1016/j.jhydrol.2022.128159_b0045 article-title: A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2016.2622301 – volume: 14 start-page: 456 year: 2009 ident: 10.1016/j.jhydrol.2022.128159_b0265 article-title: Expensive multiobjective optimization by MOEA/D with Gaussian process model publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2009.2033671 – ident: 10.1016/j.jhydrol.2022.128159_b0160 doi: 10.1016/j.jhydrol.2007.05.027 – volume: 6 start-page: 182 year: 2002 ident: 10.1016/j.jhydrol.2022.128159_b0070 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 – volume: 3 start-page: 257 year: 1999 ident: 10.1016/j.jhydrol.2022.128159_b0285 article-title: Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.797969 – ident: 10.1016/j.jhydrol.2022.128159_b0175 doi: 10.1016/j.jhydrol.2012.10.050 – volume: 94 start-page: 656 year: 2021 ident: 10.1016/j.jhydrol.2022.128159_b0260 article-title: Pore-scale simulation of salt fingers in porous media using a coupled iterative source-correction immersed boundary-lattice Boltzmann solver publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2021.01.019 – volume: 51 start-page: 5957 year: 2015 ident: 10.1016/j.jhydrol.2022.128159_b0005 article-title: A review of surrogate models and their application to groundwater modeling: surrogates of groundwater models publication-title: Water Resour. Res. doi: 10.1002/2015WR016967 – ident: 10.1016/j.jhydrol.2022.128159_b0180 doi: 10.1029/2012WR011973 – volume: 544 start-page: 591 year: 2017 ident: 10.1016/j.jhydrol.2022.128159_b0030 article-title: An efficient surrogate-based simulation-optimization method for calibrating a regional MODFLOW model publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.12.011 – volume: 220 year: 2021 ident: 10.1016/j.jhydrol.2022.128159_b0105 article-title: Surrogate-guided multi-objective optimization (SGMOO) using an efficient online sampling strategy publication-title: Knowl. Based. Syst. doi: 10.1016/j.knosys.2021.106919 – volume: 10 start-page: 50 year: 2006 ident: 10.1016/j.jhydrol.2022.128159_b0170 article-title: ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems publication-title: IEEE Trans. Evolut. Comp. doi: 10.1109/TEVC.2005.851274 – ident: 10.1016/j.jhydrol.2022.128159_b0190 doi: 10.1016/j.jhydrol.2021.126670 – ident: 10.1016/j.jhydrol.2022.128159_b0130 doi: 10.3133/tm6A16 – volume: 13 start-page: 245 year: 1998 ident: 10.1016/j.jhydrol.2022.128159_b0140 article-title: Improvements to and limitations of Latin hypercube sampling publication-title: Probab. Eng. Eng. Mech. doi: 10.1016/S0266-8920(97)00013-1 – volume: 26 start-page: 5 year: 2021 ident: 10.1016/j.jhydrol.2022.128159_b0085 article-title: Surrogate modeling approaches for multiobjective optimization: methods, taxonomy, and results publication-title: Mathem. Comput. Appl. – volume: 23 start-page: 3137 year: 2019 ident: 10.1016/j.jhydrol.2022.128159_b0050 article-title: A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms publication-title: Soft Computing doi: 10.1007/s00500-017-2965-0 – volume: 598 year: 2021 ident: 10.1016/j.jhydrol.2022.128159_b0035 article-title: Development of a surrogate method of groundwater modeling using gated recurrent unit to improve the efficiency of parameter auto-calibration and global sensitivity analysis publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2020.125726 – volume: 20 start-page: 773 year: 2016 ident: 10.1016/j.jhydrol.2022.128159_b0040 article-title: A reference vector guided evolutionary algorithm for many-objective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2016.2519378 – volume: 78 start-page: 836 year: 2007 ident: 10.1016/j.jhydrol.2022.128159_b0020 article-title: Modeling and optimization I: Usability of response surface methodology publication-title: J. Food Eng. doi: 10.1016/j.jfoodeng.2005.11.024 – volume: 1 start-page: 61 year: 2011 ident: 10.1016/j.jhydrol.2022.128159_b0150 article-title: Surrogate-assisted evolutionary computation: Recent advances and future challenges publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2011.05.001 – ident: 10.1016/j.jhydrol.2022.128159_b0250 doi: 10.1007/s10040-015-1260-3 – volume: 12 start-page: 73 year: 2017 ident: 10.1016/j.jhydrol.2022.128159_b0235 article-title: PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum] publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2017.2742868 – ident: 10.1016/j.jhydrol.2022.128159_b0220 doi: 10.1109/TEVC.2004.835247 – volume: 23 start-page: 104 year: 2018 ident: 10.1016/j.jhydrol.2022.128159_b0075 article-title: A taxonomy for metamodeling frameworks for evolutionary multiobjective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2018.2828091 – volume: 45 start-page: 81 year: 1997 ident: 10.1016/j.jhydrol.2022.128159_b0200 article-title: Artificial neural networks publication-title: Machine Learn. – ident: 10.1016/j.jhydrol.2022.128159_b0195 doi: 10.1007/s11269-019-02472-9 – volume: 8 start-page: 173 year: 2000 ident: 10.1016/j.jhydrol.2022.128159_b0280 article-title: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results publication-title: Evol. Comput. doi: 10.1162/106365600568202 |
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