Investigation the Effect of Uncertain of Outflow Determination at the Time of Flood Initiation on the Accuracy of Flood Routing Calculations Using the Linear Muskingum Method

Document Type : Technical Note (5 pages)

Authors

1 Associate Professor, Department of Civil Engineering, University of Zanjan, Zanjan, Iran.

2 Post Graduate Student of Hydraulic Structures, Department of Civil Engineering, University of Zanjan, Zanjan, Iran.

Abstract

The optimization of the Muskingum method coefficients is important for increasing the accuracy of the method. One of the main problems of this method is the estimation of the outflow rate at the time of flood initiation, which is usually equal to the inflow rate at the same time. In this study, due to the uncertainty of the equilibrium of the outflow rate at the time of flood initiation with the inflow rate at the same time, using the Particle Swarm Optimization (PSO) algorithm, in addition to calculating the coefficients of the linear Muskingum method (X, K), the outflow value in The flood initiation time is optimized in proportion to the amount of inflow at the same time, and then calculated using the calculated coefficients and the entered outflow rate is utilized using a flow that is considered as flood observations to calculate the flood outflow Other related items can be used at the same hydrometric stations. The accuracy of this method has been increased using the proposed solution in this research.

Highlights

Chow VT (1959) Open channel hydraulics. McGraw-Hill, New York, 680p

Hamedi F, Bozorg-Haddad O, Pazoki M, Asgari HR, Parsa M, Loáiciga HA (2016) Parameter estimation of extended nonlinear muskingum models with the weed optimization algorithm. Journal of Irrigation and Drainage Engineering 142(12):04016059

Mohammad Rezapour Tabari M, Emami Dehcheshmeh S (2018) Development of nonlinear Muskingum model using evolutionary algorithms hybrid. Iran Water Resources Research 14(1):160-167 (In Persian)

 

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References

Chow VT (1959) Open channel hydraulics. McGraw-Hill, New York, 680p
Hamedi F, Bozorg-Haddad O, Pazoki M, Asgari HR, Parsa M, Loáiciga HA (2016) Parameter estimation of extended nonlinear muskingum models with the weed optimization algorithm. Journal of Irrigation and Drainage Engineering 142(12):04016059
Mohammad Rezapour Tabari M, Emami Dehcheshmeh S (2018) Development of nonlinear Muskingum model using evolutionary algorithms hybrid. Iran Water Resources Research 14(1):160-167 (In Persian)
 
Iran-Water Resources Research
Volume 15, Issue 1
February 2019
Pages 356-360

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History

  • Receive Date: 24 May 2018
  • Revise Date: 29 July 2018
  • Accept Date: 04 August 2018

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