Optimization of Nonlinear Parameters of Muskingum NL5 model With SHO algorithm

Abstract

The Muskingum method was first developed by U.S. Army engineers to study flood control in the Muskingum River Basin in Ohio. To evaluate the performance of the SHO algorithm, the results of its implementation have been compared with other basic algorithms such as GA and ICA. The coding of SHO, GA and ICA algorithms was done in the MATLAB (R2018b) software programming section. The results showed that the statistical parameters obtained for the river studied by SHO algorithm in two nonlinear models of Muskingum indicate the proper performance of these algorithms in estimating the optimal values ​​of nonlinear masking modeling parameters in flood detection compared to other algorithms.

Keywords

• flood estimation
• algorithm comparison
• optimization
• nl5