Journals Books 2687-5527
Latest Issue Archive Future Issues About Us
Conference Proceedings

SETSCI - Volume 4 (6) (2019)
ISAS WINTER-2019 (ENS) - 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences, Samsun, Turkey, Nov 22, 2019

Sensitivity Analysis For Control Parameters Of Hybrid And Standard PSO Algorithms: Application Via A Rainfall-runoff Model Calibration
Umut Okkan1, Umut Kırdemir2*
1Balıkesir University, Balıkesir, Turkey
2Balıkesir University, Balıkesir, Turkey
* Corresponding author:
Published Date: 2019-12-22   |   Page (s): 336-341   |    243     4

ABSTRACT In the phases of hydrological model calibration, the impact of control parameters of optimization algorithms on the related fitness (cost) function (reaching the global solution correctly and expeditiously) is rather essential. These control parameters’ dynamic structure and interactions can force the quantification of the mentioned influence. In recent years, both the decomposition of the uncertainties of variables representing any process and searching parameter sensitivities were conducted by means of variance analysis termed as ANOVA. In the study, the success of a hybrid PSO (HPSO) algorithm, which was composed of particle swarm optimization (PSO) and a gradient-type algorithm, was shown compared to a standard PSO for a monthly rainfall-runoff model example. In addition to the measures such as reaching stable results under each run and fast convergence, individual and interactive effects of parameters of employed algorithms on simulations were also investigated by ANOVA.  In the application, different combinations were assigned for the coefficients c1 and c2 that were defined in both PSO and HPSO, and then, algorithms were run 10 times in Acisu sub-basin of Gediz Basin. The evaluations have shown that the parameters’ uncertainties due to their individual behaviors and their interactions with each other are fairly reduced in HPSO implementation. In this sense, the fact that HPSO is not excessively sensitive to its control parameters has made it a preferred choice in the hydrological model calibration process.
KEYWORDS Rainfall-runoff model calibration, PSO, HPSO, ANOVA, Dynamic parameter sensitivity analysis
REFERENCES [1] Duan, Q., Sorooshian, S., Gupta, V. (1992). Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research, 28(4), 1015-1031.
[2] Tang, Y., Reed, P., Wagener, T., (2006). How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? Hydrol. Earth Syst. Sci. 10 (2), 289–307.
[3] Arsenault, R., Poulin, A., Côté, P., Brissette, F., (2014). Comparison of stochastic optimization algorithms in hydrological model calibration. J. Hydrol. Eng. 19 (7), 1374–1384.
[4] Kirdemir, U., Okkan, U. (2019). “Determining Dynamic Sensitivity of Differential Evolution Algorithm Parameters Employed for Hydrological Model Calibration”, IV. International Conference on Civil, Environmental, Geology, and Mining Engineering (ICOCEM’19), 20 – 22 April 2019, The Convention Center of DoubleTree Hilton Hotel in Trabzon-Turkey, pp.984-990.
[5] Piotrowski, A. P., Napiorkowski, M. J., Napiorkowski, J. J., Osuch, M., Kundzewicz, Z. W. (2017). Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models? Hydrological Sciences Journal,62(4), 606-625.
[6] Qi, W., Zhang, C., Fu, G., Zhou, H. (2016). Quantifying dynamic sensitivity of optimization algorithm parameters to improve hydrological model calibration. Journal of Hydrology, 533, 213–223.

[7] Tolson, B.A., Shoemaker, C.A., (2007). Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resour. Res. 43 (1), W01413.
[8] Sobol’, I.M., (2001). Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55 (1–3), 271–280.
[9] Hadka, D., Reed, P., (2011). Diagnostic assessment of search controls and failure modes in many-objective evolutionary optimization. Evolut. Comput. 20 (3), 423–452.
[10] Yip, S., Ferro, C. A. T., Stephenson, D. B., Hawkins, E. (2011). A Simple, Coherent Framework for Partitioning Uncertainty in Climate Predictions. Journal of Climate, 24(17), 4634–4643.
[11] Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. 1942–1948.
[12] Okkan, U., Kirdemir, U. (2019). Inspection of metaheuristics for parameter estimation of conceptual rainfall-runoff models: Towards a hybrid algorithm for robust calibration. Hydrological Sciences Journal (Under Review, Date Submitted:16-June-2019).
[13] Budyko, M.I. (1958). The Heat Balance of the Earth’s Surface. US Department of Commerce, Washington, DC.
[14] Zhang, L., Potter, N., Hickel, K., Zhang, Y., Shao, Q. (2008). Water balance modeling over variable time scales based on the Budyko framework – Model development and testing. Journal of Hydrology,360(1-4), 117-131.
[15] Okkan, U., Kirdemir, U. (2018). Investigation of the Behavior of an Agricultural-Operated Dam Reservoir Under RCP Scenarios of AR5-IPCC. Water Resources Management,32(8), 2847-2866.

SET Technology - Turkey

eISSN  : 2687-5527    

E-mail :
+90 533 2245325

Tokat Technology Development Zone Gaziosmanpaşa University Taşlıçiftlik Campus, 60240 TOKAT-TURKEY
©2018 SET Technology