Skip to main content

2019 | OriginalPaper | Buchkapitel

Methods to Estimate Optimal Parameters

verfasst von : Tiantian Yang, Kuolin Hsu, Qingyun Duan, Soroosh Sorooshian, Chen Wang

Erschienen in: Handbook of Hydrometeorological Ensemble Forecasting

Verlag: Springer Berlin Heidelberg

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Model, data, and parameter estimation are three fundamental elements in hydrologic process modeling and forecasting. Recent progresses in hydrologic modeling have been made toward more efficient and effective estimation of model parameters. In this chapter, classical and recently developed parameter optimization methods and their applications in hydrological model calibration are reviewed. Those methods include gradient-based optimization methods, direct search methods, and recently developed stochastic global optimization methods. A recently developed surrogate model approach, with the purpose to reduce computational burden of model which runs through replacing the hydrologic process model with a cheaper-to-run surrogate model, is also discussed. Extending from a single objective function parameter optimization, multiobjective optimization methods and their core concept in deriving trade-offs are also summarized. Examples are provided to demonstrate the strengths and limitations of optimization algorithms summarized in this chapter.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat K. Abbaspour, R. Schulin, M.T. Van Genuchten, Estimating unsaturated soil hydraulic parameters using ant colony optimization. Adv. Water Resour. 24(8), 827–841 (2001)CrossRef K. Abbaspour, R. Schulin, M.T. Van Genuchten, Estimating unsaturated soil hydraulic parameters using ant colony optimization. Adv. Water Resour. 24(8), 827–841 (2001)CrossRef
Zurück zum Zitat M.A. Abido, Optimal design of power-system stabilizers using particle swarm optimization. IEEE Trans. Energy Convers. 17(3), 406–413 (2002)CrossRef M.A. Abido, Optimal design of power-system stabilizers using particle swarm optimization. IEEE Trans. Energy Convers. 17(3), 406–413 (2002)CrossRef
Zurück zum Zitat A. Afshar, F. Massoumi, A. Afshar, M.A. Mariño, State of the art review of ant colony optimization applications in water resource management. Water Resour. Manag. 29(11), 3891–3904 (2015)CrossRef A. Afshar, F. Massoumi, A. Afshar, M.A. Mariño, State of the art review of ant colony optimization applications in water resource management. Water Resour. Manag. 29(11), 3891–3904 (2015)CrossRef
Zurück zum Zitat D. Angus, C. Woodward, Multiple objective ant colony optimisation. Swarm Intell. 3(1), 69–85 (2009)CrossRef D. Angus, C. Woodward, Multiple objective ant colony optimisation. Swarm Intell. 3(1), 69–85 (2009)CrossRef
Zurück zum Zitat R. Arsenault, A. Poulin, P. Côté, F. Brissette, Comparison of stochastic optimization algorithms in hydrological model calibration. J. Hydrol. Eng. 19(7), 1374–1384 (2013)CrossRef R. Arsenault, A. Poulin, P. Côté, F. Brissette, Comparison of stochastic optimization algorithms in hydrological model calibration. J. Hydrol. Eng. 19(7), 1374–1384 (2013)CrossRef
Zurück zum Zitat R. Arsenault, A. Poulin, P. Côté, F. Brissette, Comparison of stochastic optimization algorithms in hydrological model calibration. J. Hydrol. Eng. 19(7), 1374–1384 (2014)CrossRef R. Arsenault, A. Poulin, P. Côté, F. Brissette, Comparison of stochastic optimization algorithms in hydrological model calibration. J. Hydrol. Eng. 19(7), 1374–1384 (2014)CrossRef
Zurück zum Zitat M. Asadzadeh, B.A. Tolson, D.H. Burn, A new selection metric for multiobjective hydrologic model calibration. Water Resour. Res. 50(9), 7082–7099 (2014)CrossRef M. Asadzadeh, B.A. Tolson, D.H. Burn, A new selection metric for multiobjective hydrologic model calibration. Water Resour. Res. 50(9), 7082–7099 (2014)CrossRef
Zurück zum Zitat V. Babovic, M. Keijzer, Rainfall runoff modelling based on genetic programming. Hydrol. Res. 33(5), 331–346 (2002)CrossRef V. Babovic, M. Keijzer, Rainfall runoff modelling based on genetic programming. Hydrol. Res. 33(5), 331–346 (2002)CrossRef
Zurück zum Zitat C. Balascio, D. Palmeri, H. Gao, Use of a genetic algorithm and multi-objective programming for calibration of a hydrologic model. Trans. ASAE 41(3), 615 (1998)CrossRef C. Balascio, D. Palmeri, H. Gao, Use of a genetic algorithm and multi-objective programming for calibration of a hydrologic model. Trans. ASAE 41(3), 615 (1998)CrossRef
Zurück zum Zitat S. Bandyopadhyay, S. Saha, U. Maulik, K. Deb, A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)CrossRef S. Bandyopadhyay, S. Saha, U. Maulik, K. Deb, A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)CrossRef
Zurück zum Zitat A. Bárdossy, T. Das, Influence of rainfall observation network on model calibration and application. Hydrol. Earth Syst. Sci. Discuss. 3(6), 3691–3726 (2006)CrossRef A. Bárdossy, T. Das, Influence of rainfall observation network on model calibration and application. Hydrol. Earth Syst. Sci. Discuss. 3(6), 3691–3726 (2006)CrossRef
Zurück zum Zitat B. Bates, Calibration of the SFB model using a simulated annealing approach. Water Down Under 94: Surface Hydrology and Water Resources Papers; Preprints of Papers, 1 (1994) B. Bates, Calibration of the SFB model using a simulated annealing approach. Water Down Under 94: Surface Hydrology and Water Resources Papers; Preprints of Papers, 1 (1994)
Zurück zum Zitat K. Behzadian, Z. Kapelan, D. Savic, A. Ardeshir, Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks. Environ. Model. Softw. 24(4), 530–541 (2009)CrossRef K. Behzadian, Z. Kapelan, D. Savic, A. Ardeshir, Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks. Environ. Model. Softw. 24(4), 530–541 (2009)CrossRef
Zurück zum Zitat E.G. Bekele, J.W. Nicklow, Multi-objective automatic calibration of SWAT using NSGA-II. J. Hydrol. 341(3), 165–176 (2007)CrossRef E.G. Bekele, J.W. Nicklow, Multi-objective automatic calibration of SWAT using NSGA-II. J. Hydrol. 341(3), 165–176 (2007)CrossRef
Zurück zum Zitat R.W. Blanning, Construction and implementation of metamodels. Simulation 24(6), 177–184 (1975)CrossRef R.W. Blanning, Construction and implementation of metamodels. Simulation 24(6), 177–184 (1975)CrossRef
Zurück zum Zitat G. Bowden, G. Dandy, H. Maier, Ant colony optimisation of a general regression neural network for forecasting water quality, in Hydroinformatics 2002: Proceedings of the FIFTH INTERNATIONAL Conference on Hydroinformatics, ed. by R.A. Falconer et al., Cardiff (IWA Publishing, 2002), pp. 692–698 G. Bowden, G. Dandy, H. Maier, Ant colony optimisation of a general regression neural network for forecasting water quality, in Hydroinformatics 2002: Proceedings of the FIFTH INTERNATIONAL Conference on Hydroinformatics, ed. by R.A. Falconer et al., Cardiff (IWA Publishing, 2002), pp. 692–698
Zurück zum Zitat L.E. Brazil, Multilevel Calibration Strategy for Complex Hydrologic Simulation Models (Colorado State University, Fort Collins, 1988) L.E. Brazil, Multilevel Calibration Strategy for Complex Hydrologic Simulation Models (Colorado State University, Fort Collins, 1988)
Zurück zum Zitat L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees (Wadsworth, Belmone, 1984) L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees (Wadsworth, Belmone, 1984)
Zurück zum Zitat R.J.C. Burnash, The NWS river forecast system: Catchment modeling, in Computer Models of Watershed Hydrology, ed. by V.P. Singh (Water Resources Publications, Highlands Ranch, 1995), pp. 311–366 R.J.C. Burnash, The NWS river forecast system: Catchment modeling, in Computer Models of Watershed Hydrology, ed. by V.P. Singh (Water Resources Publications, Highlands Ranch, 1995), pp. 311–366
Zurück zum Zitat V. Černý, Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. J. Optim. Theory Appl. 45(1), 41–51 (1985)CrossRef V. Černý, Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. J. Optim. Theory Appl. 45(1), 41–51 (1985)CrossRef
Zurück zum Zitat K. Chau, A split-step particle swarm optimization algorithm in river stage forecasting. J. Hydrol. 346(3), 131–135 (2007)CrossRef K. Chau, A split-step particle swarm optimization algorithm in river stage forecasting. J. Hydrol. 346(3), 131–135 (2007)CrossRef
Zurück zum Zitat K. Chau, Application of a particle swarm optimization algorithm to hydrological problems, in Water Resources Research Progress, (Nova Science Publishers, New York, 2008), pp. 3–12 K. Chau, Application of a particle swarm optimization algorithm to hydrological problems, in Water Resources Research Progress, (Nova Science Publishers, New York, 2008), pp. 3–12
Zurück zum Zitat C.-T. Cheng, M.-Y. Zhao, K. Chau, X.-Y. Wu, Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure. J. Hydrol. 316(1), 129–140 (2006)CrossRef C.-T. Cheng, M.-Y. Zhao, K. Chau, X.-Y. Wu, Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure. J. Hydrol. 316(1), 129–140 (2006)CrossRef
Zurück zum Zitat C.L. Chiu, J. Huang, Nonlinear time varying model of rainfall-runoff relation. Water Resour. Res. 6(5), 1277–1286 (1970)CrossRef C.L. Chiu, J. Huang, Nonlinear time varying model of rainfall-runoff relation. Water Resour. Res. 6(5), 1277–1286 (1970)CrossRef
Zurück zum Zitat W. Chu, X. Gao, S. Sorooshian, Improving the shuffled complex evolution scheme for optimization of complex nonlinear hydrological systems: Application to the calibration of the Sacramento soil-moisture accounting model. Water Resour. Res. 46(9), W09530 (2010)CrossRef W. Chu, X. Gao, S. Sorooshian, Improving the shuffled complex evolution scheme for optimization of complex nonlinear hydrological systems: Application to the calibration of the Sacramento soil-moisture accounting model. Water Resour. Res. 46(9), W09530 (2010)CrossRef
Zurück zum Zitat W. Chu, X. Gao, S. Sorooshian, A new evolutionary search strategy for global optimization of high-dimensional problems. Inf. Sci. 181(22), 4909–4927 (2011)CrossRef W. Chu, X. Gao, S. Sorooshian, A new evolutionary search strategy for global optimization of high-dimensional problems. Inf. Sci. 181(22), 4909–4927 (2011)CrossRef
Zurück zum Zitat W. Chu, T. Yang, X. Gao, Comment on “High-dimensional posterior exploration of hydrologic models using multiple-try DREAM (ZS) and high-performance computing” by Eric Laloy and Jasper A. Vrugt. Water Resour. Res. 50(3), 2775–2780 (2014)CrossRef W. Chu, T. Yang, X. Gao, Comment on “High-dimensional posterior exploration of hydrologic models using multiple-try DREAM (ZS) and high-performance computing” by Eric Laloy and Jasper A. Vrugt. Water Resour. Res. 50(3), 2775–2780 (2014)CrossRef
Zurück zum Zitat C.C. Coello, M.S. Lechuga, MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization (IEEE, Honolulu, 2002), pp. 1051–1056 C.C. Coello, M.S. Lechuga, MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization (IEEE, Honolulu, 2002), pp. 1051–1056
Zurück zum Zitat C.A.C. Coello, G.T. Pulido, M.S. Lechuga, Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)CrossRef C.A.C. Coello, G.T. Pulido, M.S. Lechuga, Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)CrossRef
Zurück zum Zitat P. Czyzżak, A. Jaszkiewicz, Pareto simulated annealing – A metaheuristic technique for multiple-objective combinatorial optimization. J. Multi-Criteria Decis. Anal. 7(1), 34–47 (1998)CrossRef P. Czyzżak, A. Jaszkiewicz, Pareto simulated annealing – A metaheuristic technique for multiple-objective combinatorial optimization. J. Multi-Criteria Decis. Anal. 7(1), 34–47 (1998)CrossRef
Zurück zum Zitat K. Deb, Multi-objective Optimization Using Evolutionary Algorithms (Wiley, Chichester, 2001) K. Deb, Multi-objective Optimization Using Evolutionary Algorithms (Wiley, Chichester, 2001)
Zurück zum Zitat K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II (Springer, Berlin, 2000), pp. 849–858 K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II (Springer, Berlin, 2000), pp. 849–858
Zurück zum Zitat K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRef K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRef
Zurück zum Zitat J.-L. Deneubourg, J.M. Pasteels, J.-C. Verhaeghe, Probabilistic behaviour in ants: A strategy of errors? J. Theor. Biol. 105(2), 259–271 (1983)CrossRef J.-L. Deneubourg, J.M. Pasteels, J.-C. Verhaeghe, Probabilistic behaviour in ants: A strategy of errors? J. Theor. Biol. 105(2), 259–271 (1983)CrossRef
Zurück zum Zitat J.-L. Deneubourg, S. Aron, S. Goss, J.M. Pasteels, The self-organizing exploratory pattern of the argentine ant. J. Insect Behav. 3(2), 159–168 (1990)CrossRef J.-L. Deneubourg, S. Aron, S. Goss, J.M. Pasteels, The self-organizing exploratory pattern of the argentine ant. J. Insect Behav. 3(2), 159–168 (1990)CrossRef
Zurück zum Zitat K. Doerner, W.J. Gutjahr, R.F. Hartl, C. Strauss, C. Stummer, Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection. Ann. Oper. Res. 131(1–4), 79–99 (2004)CrossRef K. Doerner, W.J. Gutjahr, R.F. Hartl, C. Strauss, C. Stummer, Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection. Ann. Oper. Res. 131(1–4), 79–99 (2004)CrossRef
Zurück zum Zitat M. Dorigo, Optimization, learning and natural algorithms. Ph.D. Thesis, Politecnico di Milano (in Italian) 1992 M. Dorigo, Optimization, learning and natural algorithms. Ph.D. Thesis, Politecnico di Milano (in Italian) 1992
Zurück zum Zitat M. Dorigo, C. Blum, Ant colony optimization theory: A survey. Theor. Comput. Sci. 344(2), 243–278 (2005)CrossRef M. Dorigo, C. Blum, Ant colony optimization theory: A survey. Theor. Comput. Sci. 344(2), 243–278 (2005)CrossRef
Zurück zum Zitat M. Dorigo, T. Stützle, Ant Colony Optimization: Overview and Recent Advances. Techreport, IRIDIA, Universite Libre de Bruxelles (2009) M. Dorigo, T. Stützle, Ant Colony Optimization: Overview and Recent Advances. Techreport, IRIDIA, Universite Libre de Bruxelles (2009)
Zurück zum Zitat M. Dorigo, V. Maniezzo, A. Colorni, Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B Cybern. 26(1), 29–41 (1996)CrossRef M. Dorigo, V. Maniezzo, A. Colorni, Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B Cybern. 26(1), 29–41 (1996)CrossRef
Zurück zum Zitat M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)CrossRef M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)CrossRef
Zurück zum Zitat Q. Duan, S. Sorooshian, H.V. Gupta, Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res. 28, 1015 (1992)CrossRef Q. Duan, S. Sorooshian, H.V. Gupta, Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res. 28, 1015 (1992)CrossRef
Zurück zum Zitat Q. Duan, S. Sorooshian, V.K. Gupta, Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol. 158, 265 (1994)CrossRef Q. Duan, S. Sorooshian, V.K. Gupta, Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol. 158, 265 (1994)CrossRef
Zurück zum Zitat Q. Duan, J. Schaake, V. Andreassian, S. Franks, G. Goteti, H.V. Gupta, Y.M. Gusev, F. Habets, A. Hall, L. Hay, T. Hogue, M. Huang, G. Leavesley, X. Liang, O.N. Nasonova, J. Noilhan, L. Oudin, S. Sorooshian, T. Wagener, E.F. Wood, Model parameter estimation experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. J. Hydrol. 320(1–2), 3–17 (2006)CrossRef Q. Duan, J. Schaake, V. Andreassian, S. Franks, G. Goteti, H.V. Gupta, Y.M. Gusev, F. Habets, A. Hall, L. Hay, T. Hogue, M. Huang, G. Leavesley, X. Liang, O.N. Nasonova, J. Noilhan, L. Oudin, S. Sorooshian, T. Wagener, E.F. Wood, Model parameter estimation experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. J. Hydrol. 320(1–2), 3–17 (2006)CrossRef
Zurück zum Zitat R.C. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in Micro Machine and Human Science, 1995, MHS ’95. Proceedings of the Sixth International Symposium on, Nagoya, 4–6 October 1995 (IEEE, New York, 1995), pp. 39–43. https://doi.org/10.1109/MHS.1995.494215 R.C. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in Micro Machine and Human Science, 1995, MHS ’95. Proceedings of the Sixth International Symposium on, Nagoya, 4–6 October 1995 (IEEE, New York, 1995), pp. 39–43. https://​doi.​org/​10.​1109/​MHS.​1995.​494215
Zurück zum Zitat R. Eglese, Simulated annealing: A tool for operational research. Eur. J. Oper. Res. 46(3), 271–281 (1990)CrossRef R. Eglese, Simulated annealing: A tool for operational research. Eur. J. Oper. Res. 46(3), 271–281 (1990)CrossRef
Zurück zum Zitat C. Fen, C. Chan, H. Cheng, Assessing a response surface-based optimization approach for soil vapor extraction system design. J. Water Resour. Plann. Manag. 135(3), 198–207 (2009)CrossRef C. Fen, C. Chan, H. Cheng, Assessing a response surface-based optimization approach for soil vapor extraction system design. J. Water Resour. Plann. Manag. 135(3), 198–207 (2009)CrossRef
Zurück zum Zitat F. Francés, J.I. Vélez, J.J. Vélez, Split-parameter structure for the automatic calibration of distributed hydrological models. J. Hydrol. 332(1), 226–240 (2007)CrossRef F. Francés, J.I. Vélez, J.J. Vélez, Split-parameter structure for the automatic calibration of distributed hydrological models. J. Hydrol. 332(1), 226–240 (2007)CrossRef
Zurück zum Zitat M. Franchini, Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models. Hydrol. Sci. J. 41(1), 21–39 (1996)CrossRef M. Franchini, Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models. Hydrol. Sci. J. 41(1), 21–39 (1996)CrossRef
Zurück zum Zitat M. Franchini, G. Galeati, Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models. Hydrol. Sci. J. 42(3), 357–379 (1997)CrossRef M. Franchini, G. Galeati, Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models. Hydrol. Sci. J. 42(3), 357–379 (1997)CrossRef
Zurück zum Zitat J. Friedman, Multivariate adaptive regression splines. Ann. Stat. 19(1), 1–67 (1991)CrossRef J. Friedman, Multivariate adaptive regression splines. Ann. Stat. 19(1), 1–67 (1991)CrossRef
Zurück zum Zitat T.Y. Gan, G.F. Biftu, Automatic calibration of conceptual rainfall-runoff models: Optimization algorithms, catchment conditions, and model structure. Water Resour. Res. 32(12), 3513–3524 (1996)CrossRef T.Y. Gan, G.F. Biftu, Automatic calibration of conceptual rainfall-runoff models: Optimization algorithms, catchment conditions, and model structure. Water Resour. Res. 32(12), 3513–3524 (1996)CrossRef
Zurück zum Zitat Y. Gan, Q. Duan, W. Gong, C. Tong, Y. Sun, W. Chu, A. Ye, C. Miao, Z. Di, A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model. Environ. Model. Softw. 51, 269–285 (2014)CrossRef Y. Gan, Q. Duan, W. Gong, C. Tong, Y. Sun, W. Chu, A. Ye, C. Miao, Z. Di, A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model. Environ. Model. Softw. 51, 269–285 (2014)CrossRef
Zurück zum Zitat Y. Gao, H. Guan, Z. Qi, Y. Hou, L. Liu, A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)CrossRef Y. Gao, H. Guan, Z. Qi, Y. Hou, L. Liu, A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)CrossRef
Zurück zum Zitat D.E. Golberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addion Wesley, Estados Unidos, 1989), p. 102 D.E. Golberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addion Wesley, Estados Unidos, 1989), p. 102
Zurück zum Zitat W. Gong, Q. Duan, An adaptive surrogate modeling-based sampling strategy for parameter optimization and distribution estimation (ASMO-PODE). Environ. Model Softw. 95, 61–75 (2017)CrossRef W. Gong, Q. Duan, An adaptive surrogate modeling-based sampling strategy for parameter optimization and distribution estimation (ASMO-PODE). Environ. Model Softw. 95, 61–75 (2017)CrossRef
Zurück zum Zitat W. Gong, Q. Duan, J. Li, C. Wang, Z. Di, A. Ye, C. Miao, Y. Dai, Multiobjective adaptive surrogate modeling-based optimization for parameter estimation of large, complex geophysical models. Water Resour. Res. 52(3), 1984–2008 (2016)CrossRef W. Gong, Q. Duan, J. Li, C. Wang, Z. Di, A. Ye, C. Miao, Y. Dai, Multiobjective adaptive surrogate modeling-based optimization for parameter estimation of large, complex geophysical models. Water Resour. Res. 52(3), 1984–2008 (2016)CrossRef
Zurück zum Zitat V. Granville, M. Krivánek, J.-P. Rasson, Simulated annealing: A proof of convergence. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 652–656 (1994)CrossRef V. Granville, M. Krivánek, J.-P. Rasson, Simulated annealing: A proof of convergence. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 652–656 (1994)CrossRef
Zurück zum Zitat H.V. Gupta, S. Sorooshian, P.O. Yapo, Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information. Water Resour. Res. 34(4), 751–763 (1998)CrossRef H.V. Gupta, S. Sorooshian, P.O. Yapo, Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information. Water Resour. Res. 34(4), 751–763 (1998)CrossRef
Zurück zum Zitat H.V. Gupta, S. Sorooshian, T.S. Hogue, D.P. Boyle, Advances in automatic calibration of watershed models, in Calibration of Watershed Models, (American Geophysical Union, Washington, DC, 2003), pp. 9–28CrossRef H.V. Gupta, S. Sorooshian, T.S. Hogue, D.P. Boyle, Advances in automatic calibration of watershed models, in Calibration of Watershed Models, (American Geophysical Union, Washington, DC, 2003), pp. 9–28CrossRef
Zurück zum Zitat W.K. Hastings, Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1), 97–109 (1970)CrossRef W.K. Hastings, Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1), 97–109 (1970)CrossRef
Zurück zum Zitat M.I. Hejazi, X. Cai, D.K. Borah, Calibrating a watershed simulation model involving human interference: An application of multi-objective genetic algorithms. J. Hydroinf. 10(1), 97–111 (2008)CrossRef M.I. Hejazi, X. Cai, D.K. Borah, Calibrating a watershed simulation model involving human interference: An application of multi-objective genetic algorithms. J. Hydroinf. 10(1), 97–111 (2008)CrossRef
Zurück zum Zitat J.H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (University of Michigan Press, Ann Arbor, 1975) J.H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (University of Michigan Press, Ann Arbor, 1975)
Zurück zum Zitat R. Jin, W. Chen, T.W. Simpson, Comparative studies of metamodelling techniques under multiple modeling criteria. Struct. Multidisc. Optim. 23, 1–13 (2001)CrossRef R. Jin, W. Chen, T.W. Simpson, Comparative studies of metamodelling techniques under multiple modeling criteria. Struct. Multidisc. Optim. 23, 1–13 (2001)CrossRef
Zurück zum Zitat D. Jones, A taxonomy of global optimization methods based on response surfaces. J. Glob. Optim. 21, 345–383 (2001)CrossRef D. Jones, A taxonomy of global optimization methods based on response surfaces. J. Glob. Optim. 21, 345–383 (2001)CrossRef
Zurück zum Zitat D. Jones, M. Schonlau, W. Welch, Efficient global optimization of expensive black-box functions. J. Glob. Optim. 13(4), 455–492 (1998)CrossRef D. Jones, M. Schonlau, W. Welch, Efficient global optimization of expensive black-box functions. J. Glob. Optim. 13(4), 455–492 (1998)CrossRef
Zurück zum Zitat B. Kamali, S.J. Mousavi, K.C. Abbaspour, Automatic calibration of HEC-HMS using single-objective and multi-objective PSO algorithms. Hydrol. Process. 27(26), 4028–4042 (2013)CrossRef B. Kamali, S.J. Mousavi, K.C. Abbaspour, Automatic calibration of HEC-HMS using single-objective and multi-objective PSO algorithms. Hydrol. Process. 27(26), 4028–4042 (2013)CrossRef
Zurück zum Zitat J. Kennedy, Encyclopedia of Machine Learning (Springer, Berlin, 2011), pp. 760–766 J. Kennedy, Encyclopedia of Machine Learning (Springer, Berlin, 2011), pp. 760–766
Zurück zum Zitat J. Kennedy, J.F. Kennedy, R.C. Eberhart, Y. Shi, Swarm Intelligence (Morgan Kaufmann, San Francisco, 2001) J. Kennedy, J.F. Kennedy, R.C. Eberhart, Y. Shi, Swarm Intelligence (Morgan Kaufmann, San Francisco, 2001)
Zurück zum Zitat B. Khakbaz, B. Imam, K. Hsu, S. Sorooshian, From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models. J. Hydrol. 418, 61–77 (2012)CrossRef B. Khakbaz, B. Imam, K. Hsu, S. Sorooshian, From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models. J. Hydrol. 418, 61–77 (2012)CrossRef
Zurück zum Zitat S. Kirkpatrick, Optimization by simulated annealing: Quantitative studies. J. Stat. Phys. 34(5–6), 975–986 (1984)CrossRef S. Kirkpatrick, Optimization by simulated annealing: Quantitative studies. J. Stat. Phys. 34(5–6), 975–986 (1984)CrossRef
Zurück zum Zitat P.K. Kitanidis, R.L. Bras, Real-time forecasting with a conceptual hydrologic model: 2. Applications and results. Water Resour. Res. 16(6), 1034–1044 (1980)CrossRef P.K. Kitanidis, R.L. Bras, Real-time forecasting with a conceptual hydrologic model: 2. Applications and results. Water Resour. Res. 16(6), 1034–1044 (1980)CrossRef
Zurück zum Zitat V. Kulandaiswamy, C. Subramanian, A nonlinear approach to runoff studies, in Proceedings of the International Hydrology Symposium, vol. 1, (Colorado State University, Fort Collins, 1967), pp. 72–79 V. Kulandaiswamy, C. Subramanian, A nonlinear approach to runoff studies, in Proceedings of the International Hydrology Symposium, vol. 1, (Colorado State University, Fort Collins, 1967), pp. 72–79
Zurück zum Zitat D.N. Kumar, M.J. Reddy, Ant colony optimization for multi-purpose reservoir operation. Water Resour. Manag. 20(6), 879–898 (2006)CrossRef D.N. Kumar, M.J. Reddy, Ant colony optimization for multi-purpose reservoir operation. Water Resour. Manag. 20(6), 879–898 (2006)CrossRef
Zurück zum Zitat C. Kuok, C.P. Chan, Particle swarm optimization for calibrating and optimizing Xinanjiang model parameters. Int. J. Adv. Sci. Appl. 3, 115 (2012) C. Kuok, C.P. Chan, Particle swarm optimization for calibrating and optimizing Xinanjiang model parameters. Int. J. Adv. Sci. Appl. 3, 115 (2012)
Zurück zum Zitat F. Kursawe, Parallel Problem Solving from Nature: 1st Workshop, PPSN I Dortmund, FRG, October 1–3, 1990 Proceedings, ed. by H.-P. Schwefel, R. Männer (Springer Berlin Heidelberg, Berlin, 1991), pp. 193–197 F. Kursawe, Parallel Problem Solving from Nature: 1st Workshop, PPSN I Dortmund, FRG, October 1–3, 1990 Proceedings, ed. by H.-P. Schwefel, R. Männer (Springer Berlin Heidelberg, Berlin, 1991), pp. 193–197
Zurück zum Zitat G.-F. Lin, C.-M. Wang, A nonlinear rainfall–runoff model embedded with an automated calibration method – Part 2: The automated calibration method. J. Hydrol. 341(3–4), 196–206 (2007)CrossRef G.-F. Lin, C.-M. Wang, A nonlinear rainfall–runoff model embedded with an automated calibration method – Part 2: The automated calibration method. J. Hydrol. 341(3–4), 196–206 (2007)CrossRef
Zurück zum Zitat S.Y. Liong, T.R. Gautam, S.T. Khu, V. Babovic, M. Keijzer, N. Muttil, Genetic programming: a new paradigm in rainfall runoff modeling. J. Am. Water Resour. Assoc. 38(3), 705–718 (2002)CrossRef S.Y. Liong, T.R. Gautam, S.T. Khu, V. Babovic, M. Keijzer, N. Muttil, Genetic programming: a new paradigm in rainfall runoff modeling. J. Am. Water Resour. Assoc. 38(3), 705–718 (2002)CrossRef
Zurück zum Zitat X. Liu, T. Yang, K. Hsu, C. Liu, S. Sorooshian, Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan plateau. Hydrol. Earth Syst. Sci. 21(1), 169 (2017)CrossRef X. Liu, T. Yang, K. Hsu, C. Liu, S. Sorooshian, Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan plateau. Hydrol. Earth Syst. Sci. 21(1), 169 (2017)CrossRef
Zurück zum Zitat H. Lü, T. Hou, R. Horton, Y. Zhu, X. Chen, Y. Jia, W. Wang, X. Fu, The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method. J. Hydrol. 480, 102–114 (2013)CrossRef H. Lü, T. Hou, R. Horton, Y. Zhu, X. Chen, Y. Jia, W. Wang, X. Fu, The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method. J. Hydrol. 480, 102–114 (2013)CrossRef
Zurück zum Zitat R. Ludwig, I. May, R. Turcotte, L. Vescovi, M. Braun, J.-F. Cyr, L.-G. Fortin, D. Chaumont, S. Biner, I. Chartier, The role of hydrological model complexity and uncertainty in climate change impact assessment. Adv. Geosci. 21, 63–71 (2009)CrossRef R. Ludwig, I. May, R. Turcotte, L. Vescovi, M. Braun, J.-F. Cyr, L.-G. Fortin, D. Chaumont, S. Biner, I. Chartier, The role of hydrological model complexity and uncertainty in climate change impact assessment. Adv. Geosci. 21, 63–71 (2009)CrossRef
Zurück zum Zitat S. Madadgar, A. Afshar, An improved continuous ant algorithm for optimization of water resources problems. Water Resour. Manag. 23(10), 2119–2139 (2009)CrossRef S. Madadgar, A. Afshar, An improved continuous ant algorithm for optimization of water resources problems. Water Resour. Manag. 23(10), 2119–2139 (2009)CrossRef
Zurück zum Zitat H. Madsen, Automatic calibration of a conceptual rainfall–runoff model using multiple objectives. J. Hydrol. 235(3), 276–288 (2000)CrossRef H. Madsen, Automatic calibration of a conceptual rainfall–runoff model using multiple objectives. J. Hydrol. 235(3), 276–288 (2000)CrossRef
Zurück zum Zitat H. Madsen, Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv. Water Resour. 26(2), 205–216 (2003)CrossRef H. Madsen, Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv. Water Resour. 26(2), 205–216 (2003)CrossRef
Zurück zum Zitat H. Madsen, G. Wilson, H.C. Ammentorp, Comparison of different automated strategies for calibration of rainfall-runoff models. J. Hydrol. 261(1), 48–59 (2002)CrossRef H. Madsen, G. Wilson, H.C. Ammentorp, Comparison of different automated strategies for calibration of rainfall-runoff models. J. Hydrol. 261(1), 48–59 (2002)CrossRef
Zurück zum Zitat H.R. Maier, A.R. Simpson, A.C. Zecchin, W.K. Foong, K.Y. Phang, H.Y. Seah, C.L. Tan, Ant colony optimization for design of water distribution systems. J. Water Resour. Plan. Manag. 129(3), 200–209 (2003)CrossRef H.R. Maier, A.R. Simpson, A.C. Zecchin, W.K. Foong, K.Y. Phang, H.Y. Seah, C.L. Tan, Ant colony optimization for design of water distribution systems. J. Water Resour. Plan. Manag. 129(3), 200–209 (2003)CrossRef
Zurück zum Zitat H.R. Maier, Z. Kapelan, J. Kasprzyk, J. Kollat, L.S. Matott, M. Cunha, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environ. Model Softw. 62, 271–299 (2014)CrossRef H.R. Maier, Z. Kapelan, J. Kasprzyk, J. Kollat, L.S. Matott, M. Cunha, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environ. Model Softw. 62, 271–299 (2014)CrossRef
Zurück zum Zitat R. Moussa, N. Chahinian, Comparison of different multi-objective calibration criteria using a conceptual rainfall-runoff model of flood events. Hydrol. Earth Syst. Sci. 13(4), 519–535 (2009)CrossRef R. Moussa, N. Chahinian, Comparison of different multi-objective calibration criteria using a conceptual rainfall-runoff model of flood events. Hydrol. Earth Syst. Sci. 13(4), 519–535 (2009)CrossRef
Zurück zum Zitat J.A. Nelder, R. Mead, A simplex method for function minimization. Comput. J. 7(4), 308–313 (1965)CrossRef J.A. Nelder, R. Mead, A simplex method for function minimization. Comput. J. 7(4), 308–313 (1965)CrossRef
Zurück zum Zitat V. Nourani, S. Talatahari, P. Monadjemi, S. Shahradfar, Application of ant colony optimization to optimal design of open channels. J. Hydraul. Res. 47(5), 656–665 (2009)CrossRef V. Nourani, S. Talatahari, P. Monadjemi, S. Shahradfar, Application of ant colony optimization to optimal design of open channels. J. Hydraul. Res. 47(5), 656–665 (2009)CrossRef
Zurück zum Zitat A. O’Hagan, Bayesian analysis of computer code outputs: a tutorial. Reliab. Eng. Syst. Saf. 91(10–11), 1290–1300 (2006)CrossRef A. O’Hagan, Bayesian analysis of computer code outputs: a tutorial. Reliab. Eng. Syst. Saf. 91(10–11), 1290–1300 (2006)CrossRef
Zurück zum Zitat R.E. Olarte, N. Obregon, Comparison between a simple GA and an ant system for the calibraton of a rainfall-runoff model, in 6th International Conference on Hydroinformatics (in 2 volumes, with CD-ROM) (World Scientific Publishing Company, Singapore, 2004), pp. 842–849, ISBN 981-238-787-0CrossRef R.E. Olarte, N. Obregon, Comparison between a simple GA and an ant system for the calibraton of a rainfall-runoff model, in 6th International Conference on Hydroinformatics (in 2 volumes, with CD-ROM) (World Scientific Publishing Company, Singapore, 2004), pp. 842–849, ISBN 981-238-787-0CrossRef
Zurück zum Zitat A. Ostfeld, Ant colony optimization for water resources systems analysis–Review and challenges, in Ant Colony Optimization Methods and Applications (Technion Israel Institute of Technology, Israel, 2011), p. 147CrossRef A. Ostfeld, Ant colony optimization for water resources systems analysis–Review and challenges, in Ant Colony Optimization Methods and Applications (Technion Israel Institute of Technology, Israel, 2011), p. 147CrossRef
Zurück zum Zitat M.A. Panduro, C.A. Brizuela, L.I. Balderas, D.A. Acosta, A comparison of genetic algorithms, particle swarm optimization and the differential evolution method for the design of scannable circular antenna arrays. Prog, Electromagn. Res. B 13, 171–186 (2009)CrossRef M.A. Panduro, C.A. Brizuela, L.I. Balderas, D.A. Acosta, A comparison of genetic algorithms, particle swarm optimization and the differential evolution method for the design of scannable circular antenna arrays. Prog, Electromagn. Res. B 13, 171–186 (2009)CrossRef
Zurück zum Zitat D. Pilgrim, Travel times and nonlinearity of flood runoff from tracer measurements on a small watershed. Water Resour. Res. 12(3), 487–496 (1976)CrossRef D. Pilgrim, Travel times and nonlinearity of flood runoff from tracer measurements on a small watershed. Water Resour. Res. 12(3), 487–496 (1976)CrossRef
Zurück zum Zitat J. Pintér, Continuous global optimization software: A brief review. Optima 52(1–8), 270 (1996) J. Pintér, Continuous global optimization software: A brief review. Optima 52(1–8), 270 (1996)
Zurück zum Zitat N.V. Queipo, R.T. Haftka, W. Shyy, T. Goel, R. Vaidyanathan, P. Kevin Tucker, Surrogate-based analysis and optimization. Prog. Aerosp. Sci. 41(1), 1–28 (2005)CrossRef N.V. Queipo, R.T. Haftka, W. Shyy, T. Goel, R. Vaidyanathan, P. Kevin Tucker, Surrogate-based analysis and optimization. Prog. Aerosp. Sci. 41(1), 1–28 (2005)CrossRef
Zurück zum Zitat C. Rasmussen, C. Williams, Gaussian Processes for Machine Learning (MIT Press, Cambridge, MA, 2006) C. Rasmussen, C. Williams, Gaussian Processes for Machine Learning (MIT Press, Cambridge, MA, 2006)
Zurück zum Zitat P.M. Reed, D. Hadka, J.D. Herman, J.R. Kasprzyk, J.B. Kollat, Evolutionary multiobjective optimization in water resources: The past, present, and future. Adv. Water Resour. 51, 438–456 (2013)CrossRef P.M. Reed, D. Hadka, J.D. Herman, J.R. Kasprzyk, J.B. Kollat, Evolutionary multiobjective optimization in water resources: The past, present, and future. Adv. Water Resour. 51, 438–456 (2013)CrossRef
Zurück zum Zitat R.G. Regis, C.A. Shoemaker, A stochastic radial basis function method for the global optimization of expensive functions. INFORMS J. Comput. 19, 497–509 (2007)CrossRef R.G. Regis, C.A. Shoemaker, A stochastic radial basis function method for the global optimization of expensive functions. INFORMS J. Comput. 19, 497–509 (2007)CrossRef
Zurück zum Zitat D.A. Savic, G.A. Walters, J.W. Davidson, A genetic programming approach to rainfall-runoff modelling. Water Resour. Manag. 13(3), 219–231 (1999)CrossRef D.A. Savic, G.A. Walters, J.W. Davidson, A genetic programming approach to rainfall-runoff modelling. Water Resour. Manag. 13(3), 219–231 (1999)CrossRef
Zurück zum Zitat P. Serafini, Multiple Criteria Decision Making (Springer, Berlin, 1994), pp. 283–292CrossRef P. Serafini, Multiple Criteria Decision Making (Springer, Berlin, 1994), pp. 283–292CrossRef
Zurück zum Zitat M. Shafii, F.D. Smedt, Multi-objective calibration of a distributed hydrological model (WetSpa) using a genetic algorithm. Hydrol. Earth Syst. Sci. 13(11), 2137–2149 (2009)CrossRef M. Shafii, F.D. Smedt, Multi-objective calibration of a distributed hydrological model (WetSpa) using a genetic algorithm. Hydrol. Earth Syst. Sci. 13(11), 2137–2149 (2009)CrossRef
Zurück zum Zitat A.R. Simpson, G.C. Dandy, L.J. Murphy, Genetic algorithms compared to other techniques for pipe optimization. J. Water Resour. Plan. Manag. 120(4), 423–443 (1994)CrossRef A.R. Simpson, G.C. Dandy, L.J. Murphy, Genetic algorithms compared to other techniques for pipe optimization. J. Water Resour. Plan. Manag. 120(4), 423–443 (1994)CrossRef
Zurück zum Zitat T.W. Simpson, J.D. Peplinski, P.N. Koch, J.K. Allen, Metamodels for computer-based engineering design: Survey and recommendations. Eng. Comput. 17, 129–150 (2001)CrossRef T.W. Simpson, J.D. Peplinski, P.N. Koch, J.K. Allen, Metamodels for computer-based engineering design: Survey and recommendations. Eng. Comput. 17, 129–150 (2001)CrossRef
Zurück zum Zitat K.P. Singh, Nonlinear instantaneous unit hydrograph theory. J. Hydraul. Div. Am. Soc. Civ. Eng. 90, 313–347 (1964) K.P. Singh, Nonlinear instantaneous unit hydrograph theory. J. Hydraul. Div. Am. Soc. Civ. Eng. 90, 313–347 (1964)
Zurück zum Zitat V.P. Singh, Computer Models of Watershed Hydrology (Water Resources Publications, Englewood, 1995) V.P. Singh, Computer Models of Watershed Hydrology (Water Resources Publications, Englewood, 1995)
Zurück zum Zitat B.E. Skahill, J. Doherty, Efficient accommodation of local minima in watershed model calibration. J. Hydrol. 329(1), 122–139 (2006)CrossRef B.E. Skahill, J. Doherty, Efficient accommodation of local minima in watershed model calibration. J. Hydrol. 329(1), 122–139 (2006)CrossRef
Zurück zum Zitat E. Snelson, Flexible and efficient Gaussian process models for machine learning. Ph.D. thesis, Gatsby Computational Neuroscience Unit, University College London 2007 E. Snelson, Flexible and efficient Gaussian process models for machine learning. Ph.D. thesis, Gatsby Computational Neuroscience Unit, University College London 2007
Zurück zum Zitat A. Sóbester, S. Leary, A. Keane, On the design of optimization strategies based on global response surface approximation models. J. Glob. Optim. 33(1), 31–59 (2005)CrossRef A. Sóbester, S. Leary, A. Keane, On the design of optimization strategies based on global response surface approximation models. J. Glob. Optim. 33(1), 31–59 (2005)CrossRef
Zurück zum Zitat K. Socha, M. Dorigo, Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185(3), 1155–1173 (2008)CrossRef K. Socha, M. Dorigo, Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185(3), 1155–1173 (2008)CrossRef
Zurück zum Zitat S. Sorooshian, Surface water hydrology: On-line estimation. Rev. Geophys. 21(3), 706–721 (1983)CrossRef S. Sorooshian, Surface water hydrology: On-line estimation. Rev. Geophys. 21(3), 706–721 (1983)CrossRef
Zurück zum Zitat P. Srivastava, J. Hamlett, P. Robillard, R. Day, Watershed optimization of best management practices using AnnAGNPS and a genetic algorithm. Water Res. Res. 38(3), 3-1 (2002)CrossRef P. Srivastava, J. Hamlett, P. Robillard, R. Day, Watershed optimization of best management practices using AnnAGNPS and a genetic algorithm. Water Res. Res. 38(3), 3-1 (2002)CrossRef
Zurück zum Zitat B. Suman, Study of simulated annealing based algorithms for multiobjective optimization of a constrained problem. Comput. Chem. Eng. 28(9), 1849–1871 (2004)CrossRef B. Suman, Study of simulated annealing based algorithms for multiobjective optimization of a constrained problem. Comput. Chem. Eng. 28(9), 1849–1871 (2004)CrossRef
Zurück zum Zitat N.R. Sumner, P.M. Fleming, B.C. Bates, Calibration of a modified SFB model for twenty-five Australian catchments using simulated annealing. J. Hydrol. 197(1), 166–188 (1997)CrossRef N.R. Sumner, P.M. Fleming, B.C. Bates, Calibration of a modified SFB model for twenty-five Australian catchments using simulated annealing. J. Hydrol. 197(1), 166–188 (1997)CrossRef
Zurück zum Zitat Q. Sun, D. Kong, C. Miao, Q. Duan, T. Yang, A. Ye, Z. Di, W. Gong, Variations in global temperature and precipitation for the period of 1948 to 2010. Environ. Monit. Assess. 186(9), 5663–5679 (2014)CrossRef Q. Sun, D. Kong, C. Miao, Q. Duan, T. Yang, A. Ye, Z. Di, W. Gong, Variations in global temperature and precipitation for the period of 1948 to 2010. Environ. Monit. Assess. 186(9), 5663–5679 (2014)CrossRef
Zurück zum Zitat A. Suppapitnarm, K. Seffen, G. Parks, P. Clarkson, A simulated annealing algorithm for multiobjective optimization. Eng. Optim. 33(1), 59–85 (2000)CrossRef A. Suppapitnarm, K. Seffen, G. Parks, P. Clarkson, A simulated annealing algorithm for multiobjective optimization. Eng. Optim. 33(1), 59–85 (2000)CrossRef
Zurück zum Zitat Y. Tang, P. Reed, T. Wagener, How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? Hydrol. Earth Syst. Sci. Discuss. 2(6), 2465–2520 (2005)CrossRef Y. Tang, P. Reed, T. Wagener, How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? Hydrol. Earth Syst. Sci. Discuss. 2(6), 2465–2520 (2005)CrossRef
Zurück zum Zitat M. Thyer, G. Kuczera, B.C. Bates, Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms. Water Resour. Res. 35(3), 767–773 (1999)CrossRef M. Thyer, G. Kuczera, B.C. Bates, Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms. Water Resour. Res. 35(3), 767–773 (1999)CrossRef
Zurück zum Zitat J.A. Vrugt, H.V. Gupta, W. Bouten, S. Sorooshian, A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res. 39(8), 1201–1213 (2003a). https://doi.org/10.1029/2002WR001642 J.A. Vrugt, H.V. Gupta, W. Bouten, S. Sorooshian, A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res. 39(8), 1201–1213 (2003a). https://​doi.​org/​10.​1029/​2002WR001642
Zurück zum Zitat Q. Wang, The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resour. Res. 27(9), 2467–2471 (1991)CrossRef Q. Wang, The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resour. Res. 27(9), 2467–2471 (1991)CrossRef
Zurück zum Zitat Q. Wang, Using genetic algorithms to optimise model parameters. Environ. Model Softw. 12(1), 27–34 (1997)CrossRef Q. Wang, Using genetic algorithms to optimise model parameters. Environ. Model Softw. 12(1), 27–34 (1997)CrossRef
Zurück zum Zitat Y.C. Wang, P.S. Yu, T.C. Yang, Comparison of genetic algorithms and shuffled complex evolution approach for calibrating distributed rainfall–runoff model. Hydrol. Process. 24(8), 1015–1026 (2010)CrossRef Y.C. Wang, P.S. Yu, T.C. Yang, Comparison of genetic algorithms and shuffled complex evolution approach for calibrating distributed rainfall–runoff model. Hydrol. Process. 24(8), 1015–1026 (2010)CrossRef
Zurück zum Zitat C. Wang, Q.Y. Duan, W. Gong, A.Z. Ye, Z.H. Di, C.Y. Miao, An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environ. Model. Softw. 60, 167–179 (2014)CrossRef C. Wang, Q.Y. Duan, W. Gong, A.Z. Ye, Z.H. Di, C.Y. Miao, An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environ. Model. Softw. 60, 167–179 (2014)CrossRef
Zurück zum Zitat P.A. Whigham, P.F. Crapper, Time series modelling using genetic programming: An application to rainfall-runoff models. Adv. Genet. Program 3, 89–104 (1999) P.A. Whigham, P.F. Crapper, Time series modelling using genetic programming: An application to rainfall-runoff models. Adv. Genet. Program 3, 89–104 (1999)
Zurück zum Zitat D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRef D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRef
Zurück zum Zitat C.F.J. Wu, M. Hamada, Experiments: Planning, Analysis, and Optimization, 2nd edn. (Wiley, New York, 2009) C.F.J. Wu, M. Hamada, Experiments: Planning, Analysis, and Optimization, 2nd edn. (Wiley, New York, 2009)
Zurück zum Zitat S.-J. Wu, H.-C. Lien, C.-H. Chang, Calibration of a conceptual rainfall–runoff model using a genetic algorithm integrated with runoff estimation sensitivity to parameters. J. Hydroinf. 14(2), 497–511 (2012)CrossRef S.-J. Wu, H.-C. Lien, C.-H. Chang, Calibration of a conceptual rainfall–runoff model using a genetic algorithm integrated with runoff estimation sensitivity to parameters. J. Hydroinf. 14(2), 497–511 (2012)CrossRef
Zurück zum Zitat J. Yang, P. Reichert, K.C. Abbaspour, J. Xia, H. Yang, Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. J. Hydrol. 358(1–2), 1–23 (2008)CrossRef J. Yang, P. Reichert, K.C. Abbaspour, J. Xia, H. Yang, Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. J. Hydrol. 358(1–2), 1–23 (2008)CrossRef
Zurück zum Zitat T. Yang, X. Gao, S.L. Sellars, S. Sorooshian, Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex. Environ. Model Softw. 69, 262–279 (2015)CrossRef T. Yang, X. Gao, S.L. Sellars, S. Sorooshian, Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex. Environ. Model Softw. 69, 262–279 (2015)CrossRef
Zurück zum Zitat T. Yang, X. Gao, S. Sorooshian, X. Li, Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme. Water Resour. Res. 52(3), 1626–1651 (2016)CrossRef T. Yang, X. Gao, S. Sorooshian, X. Li, Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme. Water Resour. Res. 52(3), 1626–1651 (2016)CrossRef
Zurück zum Zitat T. Yang, A.A. Asanjan, M. Faridzad, N. Hayatbini, X. Gao, S. Sorooshian, An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis. Inf. Sci. 418, 302–316 (2017a)CrossRef T. Yang, A.A. Asanjan, M. Faridzad, N. Hayatbini, X. Gao, S. Sorooshian, An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis. Inf. Sci. 418, 302–316 (2017a)CrossRef
Zurück zum Zitat T. Yang, A.A. Asanjan, E. Welles, X. Gao, S. Sorooshian, X. Liu, Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information. Water Resour. Res. 53(4), 2786–2812 (2017b)CrossRef T. Yang, A.A. Asanjan, E. Welles, X. Gao, S. Sorooshian, X. Liu, Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information. Water Resour. Res. 53(4), 2786–2812 (2017b)CrossRef
Zurück zum Zitat P.O. Yapo, H.V. Gupta, S. Sorooshian, Multi-objective global optimization for hydrologic models. J. Hydrol. 204(1), 83–97 (1998)CrossRef P.O. Yapo, H.V. Gupta, S. Sorooshian, Multi-objective global optimization for hydrologic models. J. Hydrol. 204(1), 83–97 (1998)CrossRef
Zurück zum Zitat M. Zambrano-Bigiarini, R. Rojas, A model-independent particle swarm optimisation software for model calibration. Environ. Model Softw. 43, 5–25 (2013)CrossRef M. Zambrano-Bigiarini, R. Rojas, A model-independent particle swarm optimisation software for model calibration. Environ. Model Softw. 43, 5–25 (2013)CrossRef
Zurück zum Zitat A.C. Zecchin, H.R. Maier, A.R. Simpson, A. Roberts, M.J. Berrisford, M. Leonard, Max-min ant system applied to water distribution system optimization. Proc. Int. Congr. Model. Simul. (MODSIM) 2, 795–800 (2003) A.C. Zecchin, H.R. Maier, A.R. Simpson, A. Roberts, M.J. Berrisford, M. Leonard, Max-min ant system applied to water distribution system optimization. Proc. Int. Congr. Model. Simul. (MODSIM) 2, 795–800 (2003)
Zurück zum Zitat A.C. Zecchin, A.R. Simpson, H.R. Maier, A. Marchi, J.B. Nixon, Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem. Water Resour. Res. 48(9), 795–800 (2012) A.C. Zecchin, A.R. Simpson, H.R. Maier, A. Marchi, J.B. Nixon, Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem. Water Resour. Res. 48(9), 795–800 (2012)
Zurück zum Zitat X. Zhang, R. Srinivasan, M. Van Liew, Approximating SWAT model using artificial neural network and support vector machine. J. Am. Water Resour. Assoc. 45(2), 460–474 (2009a)CrossRef X. Zhang, R. Srinivasan, M. Van Liew, Approximating SWAT model using artificial neural network and support vector machine. J. Am. Water Resour. Assoc. 45(2), 460–474 (2009a)CrossRef
Zurück zum Zitat X. Zhang, R. Srinivasan, D. Bosch, Calibration and uncertainty analysis of the SWAT model using genetic algorithms and Bayesian model averaging. J. Hydrol. 374(3), 307–317 (2009b)CrossRef X. Zhang, R. Srinivasan, D. Bosch, Calibration and uncertainty analysis of the SWAT model using genetic algorithms and Bayesian model averaging. J. Hydrol. 374(3), 307–317 (2009b)CrossRef
Zurück zum Zitat X. Zhang, R. Srinivasan, K. Zhao, M.V. Liew, Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrol. Process. 23(3), 430–441 (2009c)CrossRef X. Zhang, R. Srinivasan, K. Zhao, M.V. Liew, Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrol. Process. 23(3), 430–441 (2009c)CrossRef
Zurück zum Zitat X. Zhang, R. Srinivasan, M.V. Liew, On the use of multi-algorithm, genetically adaptive multi-objective method for multi-site calibration of the SWAT model. Hydrol. Process. 24(8), 955–969 (2010)CrossRef X. Zhang, R. Srinivasan, M.V. Liew, On the use of multi-algorithm, genetically adaptive multi-objective method for multi-site calibration of the SWAT model. Hydrol. Process. 24(8), 955–969 (2010)CrossRef
Zurück zum Zitat Q. Zhu, K.I. Hsu, Y.P. Xu, T. Yang, Evaluation of a new satellite-based precipitation data set for climate studies in the Xiang River basin, southern China. Int. J. Climatol. 37, 4561 (2017)CrossRef Q. Zhu, K.I. Hsu, Y.P. Xu, T. Yang, Evaluation of a new satellite-based precipitation data set for climate studies in the Xiang River basin, southern China. Int. J. Climatol. 37, 4561 (2017)CrossRef
Zurück zum Zitat E. Zitzler, L. Thiele, Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRef E. Zitzler, L. Thiele, Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRef
Zurück zum Zitat E. Zitzler, K. Deb, L. Thiele, Comparison of multiobjective evolutionary algorithms: Empirical results. Evol. Comput. 8(2), 173–195 (2000)CrossRef E. Zitzler, K. Deb, L. Thiele, Comparison of multiobjective evolutionary algorithms: Empirical results. Evol. Comput. 8(2), 173–195 (2000)CrossRef
Metadaten
Titel
Methods to Estimate Optimal Parameters
verfasst von
Tiantian Yang
Kuolin Hsu
Qingyun Duan
Soroosh Sorooshian
Chen Wang
Copyright-Jahr
2019
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-39925-1_26