Abstract
The tank model among various deterministic Rainfall-Runoff (RR) models is often preferred for its simple concepts by many previous studies. However, it requires much time and effort to obtain better results owing to the need to calibrate a number of parameters in the tank model. The demand for an automatic calibration method has been increasing. The success of heuristic optimization algorithms enables many researchers to focus on the other aspect of the various objective function for tank model rather than parameter calibration. In this study, Multi-objective Harmony Search Algorithm was performed for an automatic calibration. The proposed study enables parameter calibration of four storage types of tank model with 14 parameters and six scenarios based on the four objective functions. A proposed tank model, which determines optimal solution (e.g., parameters of tank model; calibration results) under four different objective functions, respectively, were compared to demonstrate tradeoff relationship between measurements data and observation data for a Daecheong dam watershed in South Korea. The proposed tank model with six different scenarios could be a successful alternative RR model with its increased accuracy and Pareto solution.
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References
Boyle, D.P., Gupta, H.V., Sorooshian, S.: Toward improved calibration of hydrological models: combining the strengths of manual and automatic methods. Water Resour. Res. 36(12), 3663–3674 (2000)
Cheng, C.T., Ou, C.P., Chau, K.W.: Combining a fuzzy optimal model with a genetic algorithm to solve multiobjective rainfall–runoff model calibration. J. Hydrol. 268, 72–86 (2002)
Cheong, C.T., Wu, X.Y., Chau, K.W.: Multiple criteria rainfall-runoff model calibration using a parallel genetic algorithm in a cluster of computers. Hydrol. Sci. J. 50(6), 1069–1087 (2005)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)
Gupta, H.V., Sorooshian, S., Yapo, P.O.: Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resour. Res. 34(4), 751–763 (1998)
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1993)
Jakeman, A.J., Hornberger, G.M.: How much complexity is warranted in a rainfall-runoff model? Water Resour. Res. 29, 2637–2649 (1993)
Kim, J.H., Geem, Z.W., Kim, E.S.: Parameter estimation of the nonlinear muskingum model using harmony search. J. Am. Water Resour. Assoc. 37(5), 1131–1138 (2001)
Yapo, P.O., Gupta, H.V., Sorooshian, S.: Multi-objective global optimization for hydrologic models. J. Hydrol. 204, 83–97 (1998)
Acknowledgment
This work was supported by a grant from The National Research Foundation (NRF) of Korea, funded by the Korean government (MSIP) (No. 2016R1A2A1A05005306).
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Kwon, S.H., Choi, Y.H., Jung, D., Kim, J.H. (2020). Multiobjective Parameter Calibration of a Hydrological Model Using Harmony Search Algorithm. In: Kim, J., Geem, Z., Jung, D., Yoo, D., Yadav, A. (eds) Advances in Harmony Search, Soft Computing and Applications. ICHSA 2019. Advances in Intelligent Systems and Computing, vol 1063. Springer, Cham. https://doi.org/10.1007/978-3-030-31967-0_9
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DOI: https://doi.org/10.1007/978-3-030-31967-0_9
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