Optimization of the Nonlinear Muskingum Model Parameters for the River Routing, Tigris River a Case Study

Flood forecasting and management are one of the most important strategies necessary for water resource decision planners in combating flood problems. The Muskingum model is popular widely used applications purpose predicting routing. particle swarm optimization (PSO) methodology was to estimate coefficients nonlinear this study, comparing results with methods genetic algorithm (GA), harmony search (HS), least-squares method (LSM), Hook-Jeeves (HJ). average monthly inflow Tigris River upstream at Al-Mosul dam selected as a case study estimating model's parameters. analytical statistical showed that PSO best application corresponds model, followed by method, according following general descending sequence: PSO, GA, LSM, HJ, HS. characterized its accurate does not require many assumptions conditions application, which facilitates use lot subject hydrology. Therefore, it better recommend further research implementation future studies applications.