Document Type : Research Paper


Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran.


In engineering works, calculation of the peak zone of the flood is very important. Therefore, in the present study, a method was presented to increase the accuracy of the flood routing of the peak zone of the inflow hydrograph. The recorded data in the Ahwaz and Farsiat hydrometric stations were used, both of which are related to the Karun river, Iran. In contrast to previous studies, in addition to calculating the coefficients of linear Muskingum method (X, K), the time interval (Δt) parameter was also optimized in the present study using the PSO algorithm. The results showed that if only the X and K coefficients were calculated, the mean relative error (MRE) of the peak zone for the first, second and third floods were equal to 8.34, 2.24, and 1.99 %, respectively. However, by using the optimized Δt value, the corresponding error decreased to 5.14, 0.44, and 1.08 %.


Abozari N., Hassanvand M., Salimi A.H., Heddam S., Mohammadi H.O.,
Noori A., Comparison performance of artificial neural network based
method in estimation of electric conductivity in wet and dry periods:
Case study of Gamasiab river, Iran, Journal of Applied Research in
Water and Wastewater 6 (2019) 88-94.
Afshar A., Kazemi H., Saadatpour M., Particle swarm optimization for
automatic calibration of large scale water quality model (CE-QUALW2):
Application to Karkheh reservoir, Iran, Water Resources
Management 25 (2011) 2613-2632.
Bazargan J., and Norouzi H., Investigation the effect of using variable
values for the parameters of the linear muskingum method using the
particle swarm algorithm (PSO), Water Resources Management 32
(2018) 4763-4777.
Chau K., A split-step PSO algorithm in prediction of water quality
pollution, In International Symposium on Neural Networks, Springer,
Berlin, Heidelberg (2005) 1034-1039.