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Investigation of effect of optimal time interval on the linear Muskingum method using particle swarm optimization algorithm | ||
| Journal of Applied Research in Water and Wastewater | ||
| مقاله 7، دوره 7، شماره 2 - شماره پیاپی 14، اسفند 2020، صفحه 152-156 اصل مقاله (1.16 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22126/arww.2021.4426.1137 | ||
| نویسندگان | ||
| Hadi Norouzi* ؛ Jalal Bazargan | ||
| Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran. | ||
| چکیده | ||
| 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 %. | ||
| کلیدواژه‌ها | ||
| Flood routing؛ Linear Muskingum method؛ Optimization of Δt parameter؛ PSO algorithm | ||
| مراجع | ||
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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. | ||
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