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

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.

Automated summary

Considering the cost and time, the Muskingum method proves to be the most efficient flood routing technique. This paper proposes a new hybrid Muskingum model with a nonlinear Muskingum model and a new lateral flow equation, which significantly improves the accuracy of flood routing compared to previous studies. The artificial gorilla troops optimizer is utilized to approximate the model decision variables, and the results of six case studies demonstrate the effectiveness of the new proposed method, especially in the Dinavar River flood, which has not been studied before. The outstanding performance of the artificial gorilla troops algorithm in minimizing the target function is another noteworthy point.