A new technique for flood routing by nonlinear Muskingum model and artificial gorilla troops algorithm

Abstract Considering cost and time, the Muskingum method is the most efficient flood routing technique. The existing Muskingum models are only different in the storage equation and their efficiency depends on the model type and the estimation of different parameters. In this paper, the nonlinear Muskingum model is combined with a new lateral flow equation. Although the new lateral flow equation includes five decision variables, flood routing is done more accurately than previous studies. The new hybrid Muskingum model have 12 decision variables. To approximate the model decision variables, the artificial gorilla troops optimizer is utilized. The new Muskingum is examined for six case studies. The results of the new proposed method for these studies indicates the significant improvement of the model compared to previous research. Moreover, the sixth case study is the Dinavar River flood, which has not been used by researchers so far. Another significant point is the outstanding performance of the powerful artificial gorilla troops algorithm in minimizing the target function.