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USING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR PARAMETER ESTIMATION IN HYDROLOGICAL MODELLING

Jakubcova, Michala

First published: 2015https://doi.org/10.5593/sgem2015/b21/s7.050View metrics

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Title
USING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR PARAMETER ESTIMATION IN HYDROLOGICAL MODELLING
Authors
Jakubcova, Michala
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 15th International Multidisciplinary Scientific GeoConference SGEM2015, INFORMATICS, GEOINFORMATICS AND REMOTE SENSING
Publisher
Stef92 Technology
Year
2015
Pages
399-406
ISSN
1314-2704
ISBN
978-619-7105-34-6
Language
en
Publication type
Conference Paper
References28

  1. Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation, Washington, DC, pp 1951–1957, 1999. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org 15th International Multidisciplinary Scientific GeoConferences SGEM2015

  2. Derrac J. & Garcia S. & Molina D. & Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evolution ary Computation, vol. 1/issue 1, pp 3– 18, 2011.

  3. Duan Q.Y. & Gupta V.K. & Sorooshian S. Shuffled complex evolution approach for effective and efficient global minimization, Journal of Optimization Theory and Applications, vol. 76/issue 3, pp 501– 521, 1993.

  4. Duan Q. & Schaake J. & Andreassian V. [eds]. Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops, Journal of Hydrology, vol. 320/issue 1, pp 3 – 17, 2006.

  5. Jakubcová M. & Máca P. & Pech P. A comparison of selected modifications of the particle swarm optimization algorithm, Journal of Applied Mathematics, vol. 2014, pp 1 –10, 2014.

  6. Jiang Y. & Li X. & Huang C. Automatic calibration a hydrological model using a master-slave swarms shuffling evolution algorithm based on selfadaptive particle swarm optimization, Expert Systems with Applications: An International Journal, vol. 40/issue 2, pp 752– 757, 2013.

  7. Kašpárek L. & Hanel M. & Horáček S. & Máca P. & Vizina A. bilan: Bilan water balance model, T. G. Masaryk Water Research Institute and p.r.i., R package version 2013.12, 2014.

  8. Kennedy J. & Eberhart R. Particle swarm optimization, Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, WA, pp 1942 –194 8, 1995.

  9. Shi Y. & Eberhart R. A modified particle swarm optimizer, IEEE International Conference on Evolutionary Computation, Anchorage, UK, pp 69–73, 1998.

  10. Shi Y. & Eberhart R. Empirical study of particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation, Washington, DC, pp 1945– 1950, 1999.

  11. Simon D. Evolutionary optimization algorithms, John Wiley & Sons, 2013.

  12. Tallaksen L. & Van Lanen H. Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater, Vol. 48 of Developments in water science, Elsevier, 2004.

  13. Van Lanen H. & Tallaksen L. & Kašpárek L. & Querner E. Hydrological drought analysis in the Hupsel basin using different physically-based models, in: Gustard A. [eds]. FRIEND’97 -Regional Hydrology: Concepts and Models for Sustainable Water Resource Management, no. 246, UNESCO; WMO; EC; IAHS; MST Slovenia, pp 189196, 1997.

  14. Zambrano-Bigiarini M. & Rojas R. A model-independent Particle Swarm Optimisation software for model calibration, Environmental Modelling & Software, vol. 43, pp 5– 25, 2013. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org

  15. Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation, Washington, DC, pp 1951–1957, 1999. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org 15th International Multidisciplinary Scientific GeoConferences SGEM2015

  16. Derrac J. & Garcia S. & Molina D. & Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evolution ary Computation, vol. 1/issue 1, pp 3– 18, 2011.

  17. Duan Q.Y. & Gupta V.K. & Sorooshian S. Shuffled complex evolution approach for effective and efficient global minimization, Journal of Optimization Theory and Applications, vol. 76/issue 3, pp 501– 521, 1993.

  18. Duan Q. & Schaake J. & Andreassian V. [eds]. Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops, Journal of Hydrology, vol. 320/issue 1, pp 3 – 17, 2006.

  19. Jakubcová M. & Máca P. & Pech P. A comparison of selected modifications of the particle swarm optimization algorithm, Journal of Applied Mathematics, vol. 2014, pp 1 –10, 2014.

  20. Jiang Y. & Li X. & Huang C. Automatic calibration a hydrological model using a master-slave swarms shuffling evolution algorithm based on selfadaptive particle swarm optimization, Expert Systems with Applications: An International Journal, vol. 40/issue 2, pp 752– 757, 2013.

  21. Kašpárek L. & Hanel M. & Horáček S. & Máca P. & Vizina A. bilan: Bilan water balance model, T. G. Masaryk Water Research Institute and p.r.i., R package version 2013.12, 2014.

  22. Kennedy J. & Eberhart R. Particle swarm optimization, Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, WA, pp 1942 –194 8, 1995.

  23. Shi Y. & Eberhart R. A modified particle swarm optimizer, IEEE International Conference on Evolutionary Computation, Anchorage, UK, pp 69–73, 1998.

  24. Shi Y. & Eberhart R. Empirical study of particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation, Washington, DC, pp 1945– 1950, 1999.

  25. Simon D. Evolutionary optimization algorithms, John Wiley & Sons, 2013.

  26. Tallaksen L. & Van Lanen H. Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater, Vol. 48 of Developments in water science, Elsevier, 2004.

  27. Van Lanen H. & Tallaksen L. & Kašpárek L. & Querner E. Hydrological drought analysis in the Hupsel basin using different physically-based models, in: Gustard A. [eds]. FRIEND’97 -Regional Hydrology: Concepts and Models for Sustainable Water Resource Management, no. 246, UNESCO; WMO; EC; IAHS; MST Slovenia, pp 189196, 1997.

  28. Zambrano-Bigiarini M. & Rojas R. A model-independent Particle Swarm Optimisation software for model calibration, Environmental Modelling & Software, vol. 43, pp 5– 25, 2013. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org

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