How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?

Hydrology and Earth System Sciences

Volumn 10, Issue 2, 2006, Pages 289-307

  Tang, Y ^a   Reed, P ^a   Wagener, T ^a  

^a The Pennsylvania State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; CALIBRATION; HYDROLOGICAL MODELING;

LEAF RIVER; MISSISSIPPI; NORTH AMERICA; PENNSYLVANIA; SHALE HILLS; UNITED STATES;

EID: 33845307599     PISSN: 10275606     EISSN: 16077938     Source Type: Journal    
DOI: 10.5194/hess-10-289-2006     Document Type: Article

References (66)

1. Ajami, N. K., Gupta, H. V., Wagener, T., and Sorooshian, S.: Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system, J. Hydrol., 298, 112-135, 2004.
2. Boyle, D. P., Gupta, H. V., and Sorooshian, S.: Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods, Water Resour. Res., 36, 3663-3674, 2000.
3. Burnash, R. J. C.: The NWS river forecast system-Catchment model, in: Computer Model s of Watershed Hydrology, edited by: Singh, V. P., Water Resources Publications, Highlands Ranch, CO, 1995.
4. Carsel, R. F.: Developing joint probability distributions of soil water retention characteristics, Water Resour. Res., 24, 755-769, 1988.
5. Cieniawski, S. E., Eheart, J. W., and Ranjithan, S. R.: Using genetic algorithms to solve a multiobjective groundwater monitoring problem, Water Resour. Res., 31, 399-409, 1995.
6. Coello Coello, C., Van Veldhuizen, D. A., and Lamont, G. B.: Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer Academic Publishers, New York, 2002.
7. Conover, W. J.: Practical Nonparametric Statistics, 3rd edition, John Wiley & Sons, New York, 1999.
8. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons LTD., New York, 2001.
9. Deb, K. and Jain, S.: Running Performance Metrics for Evolutionary Multi-Objective Optimization, 2002004, Indian Institute of Technology Kanpur, Kanpur, India, 2002.
10. Deb, K., Mohan, M., and Mishra, S.: A Fast Multi-objective Evolutionary Algorithm for Finding Well-Spread Pareto-Optimal Solutions, KanGAL Report No. 2003002, Indian Institute of Technology, Kanpur, India, 2003.
11. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II, IEEE Trans. Evol. Computation, 6, 182-197, 2002.
12. Deb, K., Thiele, L., Laumanns, M., and Zitzler, E.: Scalable multi-objective optimization test problems, in: In Proceedings of the Congress on Evolutionary Computation (CEC-2002), pp. 825-830, 2002.
13. Dingman, S. L.: Physical Hydrology, Second Edition edition, Prentice Hall, New Jersey, 2002.
14. Duan, Q., Gupta, V. K., and Sorooshian, S.: Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015-1031, 1992.
15. Duan, Q., Gupta, V. K., and Sorooshian, S.: A shuffled complex evolution approach for effective and efficient global minimization, J. Optimiz. Theory App., 76, 501-521, 1993.
16. Duffy, C. J.: A two-state integral-balance model for soil moisture and groundwater dynamics in complex terrain, Water Resour. Res., 32, 2421-2434, 1996.
17. Duffy, C. J.: A Distributed-Dynamical Model for Mountain-Front Recharge & Water Balance Estimation: The Rio Grande of Southern Colorado and New Mexico, in: AGU Monograph: Recharge in Semi-Arid Regions: State of the Art, edited by: Scanlon, H. a., 2004.
18. Erickson, M. A., Mayer, A., and Horn, J.: Multi-objective optimal design of groundwater remediation systems: application of the niched Pareto genetic algorithm (NPGA), Adv. Water Resour., 25, 51-56, 2002.
19. Fonseca, C. M., Knowles, J. D., Thiele, L., and Ziztler, E.: A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers, in: Evolutionary Multi Criterion Optimization: Third International Conference (EMO 2005), Guanajuato, Mexico, 2005.
20. Gan, T. Y. and Biftu, G. F.: Automatic calibration of conceptual rainfall-runoff models: optimization algorithms, catchment conditions, and model structure, Water Resour. Res., 32, 3513-3524, 1996.
21. Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Company, Reading, MA, 1989.
22. Goldberg, D. E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms, Kluwer Academic Publishers, Norwell, MA, 2002.
23. Guglielmi, N. and Hairer, E.: Implementing Radau IIA methods for stiff delay differential equations, Computing, 67, 1-12, 2001.
24. Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34, 751-763, 1998.
25. Hairer, E. and Wanner, G.: Solving Ordinary Differential Equations II - Stiff and Differential-Algebraic Problems, Second Revised Edition, Springer, Verlag, 1996.
26. Halhal, D., Walters, G. A., Ouazar, D., and Savic, D. A.: Water network rehabilitation with structured messy genetic algorithm, J. Water Res. Pl., 123, 137-146, 1997.
27. Horn, J. and Nafpliotis, F: Multiobjective Optimization using the Niched Pareto Genetic Algorithm, IlliGAL Report No. 93005, University of Illinois, Urbana, IL, 1993.
28. Ishibuchi, H. and Narukawa, K.: Comparision of local search implementation schemes in hybrid evolutionary multiobjective optimization algorithms, in: Hybrid Intelligent Systems (HIS 2004): Fourth International Conference, IEEE Press, Kitakyushu, Japan, 404-409, 2004.
29. Kollat, J. B. and Reed, P.: The Value of Online Adaptive Search: A comparison of NSGA-II, ε-NSGAII, and R MOEA, in: Evolutionary Multi Criterion Optimization: Third International Conference (EMO 2005), edited by: Coello Coello, C., Hernandez, A., and Zitzler, E., Lecture Notes in Computer Science, Springer-Verlag, Guanajuato, Mexico, 386-398, 2005a.
30. Kollat, J. B. and Reed, P.: Comparing State-of-the-Art Evolutionary Multi-Objective Algorithms for Long-Term Groundwater Monitoring Design, Adv. Water Resour., 29, 792-807, 2005b.
31. Krasnogor, N. and Smith, J.: A Tutorial for Competent Memetic Algorithms: Model, Taxonomy, and Design Issues, IEEE Trans. Evol. Comput., 9, 474-488, 2005.
32. Kuczera, G.: Efficient subspace probabilistic parameter optimization for catchment models, Water Resour. Res., 33, 177-185, 1997.
33. Laumanns, M., Thiele, L., Deb, K., and Zitzler, E.: Combining Convergence and Diversity in Evolutionary Multiobjective Optimization, Evol. Comput., 10, 263-282, 2002.
34. Lin, H. S., Kogelmann, W., Walker, C., and Bruns, M. A.: Soil moisture patterns in a forested catchment: A hydropedological perspective, Geoderma, 131, 345-368, 2005.
35. Loughlin, D. H., Ranjithan, S. R., Baugh Jr., J. W, and Brill Jr., E. D.: Application of Genetic Algorithms for the Design of Ozone Control Strategies, J. Air Waste Manage. Assoc., 50, 1050-1063, 2000.
36. Madsen, H.: Automatic calibration of a conceptual rainfall-ruoff model using multiple objectives, J. Hydrol., 235, 276-288, 2000.
37. Madsen, H.: Parameter estimation in distributed hydrological catchment modeling using automatic calibration with multiple objectives, Adv. Water Resour., 26, 205-216, 2003.
38. Madsen, H., Wilson, G., and Ammentorp, H. C.: Comparison of different automated strategies for calibration of rainfall-runoff models, J. Hydrol., 261, 48-59, 2002.
39. Misirli, F., Gupta, H. V., Sorooshian, S., and Thiemann, M.: Bayesian recursive estimation of parameter and output uncertainty for watershed models, in: Calibration of Watershed Models, edited by: Duan, Q., Gupta, H. V., Sorooshian, S., et al., American Geophysical Union, Washington, D.C., pp. 113-124, 2003.
40. Muleta, M. and Nicklow, J.: Decision Support for Watershed Management Using Evolutionary Algorithms, J. Water Res. P1., 131, 35-44, 2005a.
41. Muleta, M. K. and Nicklow, J. W.: Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model, J. Hydrol., 306, 127-145, 2005b.
42. Pareto, V.: Cours D'Economie Politique, Rouge, Lausanne, 1896.
43. Qu, Y.: An integrated hydrologic model for multi-process simulation using semi-discrete finite volume approach, PhD dissertation, Penn State University, University Park, PA, 2004.
44. Reed, P. and Minsker, B. S.: Striking the Balance: Long-Term Groundwater Monitoring Design for Conflicting Objectives, J. Water Res. Pl., 130, 140-149, 2004.
45. Reed, P., Minsker, B. S., and Goldberg, D. E.: A multiobjective approach to cost effective long-term groundwater monitoring using an Elitist Nondominated Sorted Genetic Algorithm with historical data, J. Hydroinform., 3, 71-90, 2001.
46. Reed, P., Minsker, B. S., and Goldberg, D. E.: Simplifying Multiobjective Optimization: An Automated Design Methodology for the Nondominated Sorted Genetic Algorithm-II, Water Resour. Res., 39, 1196, doi:1110.1029/2002WR001483, 2003.
47. Ritzel, B. J., Eheart, J. W., and Ranjithan, S. R.: Using genetic algorithms to solve a multiple objective groundwater pollution Containment problem, Water Resour. Res., 30, 1589-1603, 1994.
48. Schaffer, J. D.: Some experiments in machine learning using vector evaluated genetic algorithms, Doctoral Thesis, Vanderbilt University, Nashville, TN, 1984.
49. Schwefel, H.-P.: Evolution and Optimum Seeking, Computer Disk Edition edition, John Wiley & Sons, Inc., New York, 1995.
50. Seibert, J.: Multi-criteria calibration of a conceptual runoff model using a genetic algorithm, Hydrol. Earth Syst. Sci., 4, 215-224, 2000.
51. Solomatine, D.P.: Genetic and other global optimization algorithms - comparison and use in calibration problems, in: Proc. 3rd International Conference on Hydroinformatics, Copenhagen, Denmark, 1021-1028, 1998.
52. Solomatine, D.P.: Two strategies of adaptive cluster covering with descent and their comparison to other algorithms, J. Global Optim., 14(1), 55-78, 1999.
53. Solomatine, D.P.: Adaptive cluster covering and evolutionary approach: comparison, differences and similarities, in: Proc. IEEE Congress on Evolutionary Computation (CEC-2005), Edinburgh, U.K., 1959-1966, 2005.
54. Sorooshian, S. and Dracup, J. A.: Stochastic parameter estimation procedures for hydrologic rainfall-runoff models: correlated and heteroscedastic error cases, Water Resour. Res., 16, 430-442, 1980.
55. Sorooshian, S., Duan, Q., and Gupta, H. V.: Calibration of rainfall-runoff models: Application of global optimization to the Sacramento soil moisture accounting model, Water Resour. Res., 29, 1185-1194, 1993.
56. Vrugt, J., Gupta, H. V., Bastidas, L. A., Bouten, W., and Sorooshian, S.: Effective and efficient algorithm for multiobjective optimization of hydrologic models, Water Resour. Res., 39, 1214, doi: 1210.1029/2002WR001746, 2003a.
57. Vrugt, J., Gupta, H. V., Bouten, W., and Sorooshian, S.: A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39, 1201, doi:1210.1029/2002WR001642, 2003b.
58. Vrugt, J. A., Schoups, G., Hopmans, J. W., Young, C., Wallender, W. W., and Harter, T.: Inverse modeling of large-scale spatially distributed vadose zone properties using global optimization, Water Resour. Res., 41, W06003, doi:10.1029/2004WR003698, 2005.
59. Wagener, T., Boyle, D. P., Lees, M. J., Wheater, H. S., Gupta, H. V., and Sorooshian, S.: A framework for development and application of hydrological models, Hydrol. Earth Syst. Sci., 5, 13-26, 2001.
60. Wagener, T. and Gupta, H. V.: Model Identification for hydrological forecasting under uncertainty, Stoch. Env. Res. Risk A., 19 (6), 378-387, doi:10.1007/s00477-005-006-5, 2005.
61. Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data, J. Hydrol., 181, 23-48, 1996.
62. Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Multi-objective global optimization for hydrologic models, J. Hydrol., 204, 83-97, 1998.
63. Zitzler, E., Deb, K., and Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results, Evol. Comput., 8, 125-148, 2000.
64. Zitzler, E., Laumanns, M., and Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm, TIK-103, Department of Electrical Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland, 2001.
65. Zitzler, E. and Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach, IEEE Trans. Evol. Computation, 3, 257-271, 1999.
66. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., and Grunert da Fonseca, V.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review, IEEE Trans. Evol. Computation, 7, 117-132, 2003.