Presented at the 2nd International Symposium on Innovative Approaches in Scientific Studies (ISAS2018-Winter), Samsun, Turkey, Nov 30, 2018

SETSCI Conference Proceedings, 2018, 3, Page (s): 437-439 , https://doi.org/

Published Date: 31 December 2018    | 1271     10

In the analysis of the flood routing problem, it is necessary to determine the change of the flood wave moving
through the channel according to time and location, and in this context, nonlinear 3-parameter Muskingum model (NL-MUSK)
is widely used. The parameters of the NL-MUSK model should be estimated precisely to hold the appropriate solutions. In
general, this important process is solved by the use of heuristic methods, one of which is the Particle Swarm Optimization
(PSO).This study aims to improve searching the local solution capability among the possible global solutions revealed by the
particle swarm optimization (PSO) in a NL-MUSK flood rooting problem. To this end, the hybrid use of the PSO with the
Levenberg-Marquardt (LM) algorithm, based on Jacobian matrix, was used. The developed algorithm (PSO-LM) was applied
to the flood data observed in 1960 on the River Wye, U.K. The hybrid PSO-LM algorithm was operated under a wide solution
space and the same global solution was obtained from each random experiment. The PSO-LM, which stands out with its stable
aspect, has also achieved rapid convergence compared to the PSO, in other words, it has accelerated the access of the model to
the optimum result with less iteration. In this respect, it is concluded that PSO-LM can be adapted effectively to different
hydrological modeling studies.  

Keywords - Nonlinear Muskingum model, Hybrid heuristic methods, Parameter estimation, Levenberg-Marquardt algorithm

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