Evaluation of the performance of bat algorithm in optimization of nonlinear Muskingum model parameters for flood routing

Document Type : Research Article

Authors

1 PhD candidate, Faculty of Civil Engineering, Semnan University, Semnan, Iran

2 Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran

3 Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran

4 PhD candidate, Faculty of Civil Engineering, Chamran University, Ahvaz, Iran

Abstract

In this study, bat algorithm is used as an algorithm based on velocity and location of bats to optimize the parameters of Muskingum's nonlinear model for flood routing. The case study of Wilson flood as well as a historical flood from Lighvan area were selected for flood routing and calculating the parameters of Muskingum's model, with the aim of examining the efficiency of this algorithm. The sum of squares of deviations and the sum of the absolute values of deviations between routed and observational flows were considered as the objective functions. According to the results obtained from the Wilson flood routing using the bat algorithm, the values of these objective functions are equal to 35.14 and 22.76 m3 per second, respectively. The results of routing of Lighvan flood by using bat algorithm also indicated that the sum of squared deviations, the sum of absolute values of deviations, and the difference between observed and routed peak flows are equal to 7.24, 6.23 and 0 m3/s, respectively. In the present study, the performance of the bat algorithm was compared with evolutionary algorithms such as genetic, particle swarm, and harmony algorithms.

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References

[1]. Das, A.. Parameter estimation for Muskingum models. Journal of Irrigation and Drainage Engineering. ASCE. (2004); 130(2): 140-147.
[2]. Ponce, V.M. and Lugo, A.. Modeling looped ratings in Muskingum-Cunge routing. Journal of Hydrologic Engineering (ASCE). (2001); 6(2): 119-124.
                                                   
[3]. Chu, H.J. and Chang L.C.. Applying particle swarm optimization to parameter estimation of the nonlinear Muskingum model. Journal of Hydrologic Engineering (ASCE). (2009); 14(9):1024-1027.
 
[4]. Al-Hummed, J.M. and Essen, I.I. Approximate methods for the estimation of Muskingum flood routing parameters. Water Resources Management. (2006); 20: 979-990.
 
[5]. Geem, Z..Parameter Estimation for the Nonlinear Muskingum Model using the BFGS Technique. Irrigation and Drainage Engineering. ASCE. (2006); 132(5): 474-478.
 
[6]. Wang, G.T. and Chen, S. A semianalytical solution of the Saint-Venant equations for channel flood routing. Journal ofWater Resources Research. (2003); 39(4): 1-10.
 
[7]. Mohan, S. Parameter estimation of nonlinear Muskingum models using Genetic Algorithm. Hydraulic Engineering (ASCE). (1997);132(2): 137-142.
 
[8]. Xu, D.M., Qiu, L. and Chen, S.Y. Estimation of nonlinear Muskingum model parameter using differential evolution. Journal of Hydrologic Engineering (ASCE). (2011);17: 348-353.
 
[9]. Kim, J.H., Geem, Z.W. and Kim, E.S.. Parameter estimation of the nonlinear Muskingum model using Harmony Search, Journal of The American Water Resources Association. (2001);37: 1131-1138.
 
[10]. Orouji, H., Bozorg-Haddad, O., Fallah-Mehdipour, E. and Mariño, M.A.,. Flood routing in branched river by genetic programming. Water Management. (2012); 167(2): 115-123.
 
[11]. Ouyang, A., Liu, L. and Li, K.. GPU-based variation of parallel invasive weed optimization algorithm for 1000D functions. Natural Computation (ICNC). 10th International Conference. (2014);19-21 August. Xiamen.
 
[12]. Yang, X.S. and Gandomi, A.H. Bat algorithm: A novel approach for global engineering optimization. Engineering Computations. (2012); 29(5): 464–483.
 
[13]. Ahmadianfar, I., Adib, A., and Salarijazi, M. Optimizing multireservoir operation: Hybrid of bat algorithm and differential evolution. J. Water Resour. Plann. Manage. (2015);, 10.1061/(ASCE)WR.1943-5452.
 
[14]. Yang, X.S. A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NISCO 2010), J. R. Gonzalez, et al. Eds., Springer, Berlin, (2010) 284: 65-74.
 
[15]. Yang, X.S. Meta-heuristic optimization with applications: Demonstration via bat algorithm. Proc. 5th Bioinspired Optimization Methods and Their Applications (BIOMA2012), Bohinj, Slovenia, pp. (2012); 23–34.
 
[16]. Yoon, J.W. and Padmanabhan, G. Parameter estimation of linear and nonlinear Muskingum models. Water Resources Planning and Management (ASCE). (1993); 119(5): 600-610.
 
[17]. Ghafari, A., Fakheri, A.Flood routing based on hydraulic model and hydrologic model. Water and soil. (2011); 201 (3):48-70 (In Persian).
 
[18]. Barati, R. Discussion of ‘Parameter estimation of the nonlinear Muskingum model using parameter-setting-free harmony search’ by Z. W. Geem. J. Hydrology. (2012);.1943-5584
.
[19]. Barati, R. Application of Excel solver for parameter estimation of the nonlinear Muskingum models. KSCE J. Civil. Engineering., (2013);17(5), 1139–1148.
 
[20]. Barati, R. Closure to ‘Parameter estimation of nonlinear Muskingum model using Nelder-Mead simplex algorithm’ by R. Barati. J. Hydrol. Eng. (2013);, 367–370.
 
[21]. Easa, S. M. Multi-criteria optimisation of the Muskingum flood model: A new approach. Proc. ICE Water Manage., (In Persian). (2014). 16(4):214-228
 
[22]. Easa, S. M. Versatile Muskingum flood model with four variable parameters. Proc. ICE - Water Manage., (2014);168(3): 139–148.
Iranian journal of Ecohydrology
Volume 4, Issue 4
January 2018
Pages 1025-1032

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History

  • Receive Date: 25 March 2017
  • Revise Date: 15 August 2017
  • Accept Date: 05 August 2017
  • First Publish Date: 22 December 2017
  • Publish Date: 22 December 2017

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