Estimation of Optimal Parameters of the Nonlinear Muskingum Model Using Continuous Ant Colony Algorithm

Optimizing parameters of nonlinear Muskingum model done by the method of try and error and numerical. These methods are difficult and time-consuming. But Meta-Heuristic Algorithms can indicate the good estimation of these parameters with high-speed and more precision. In this study investigated performance of continuous ant colony algorithm (ACOr) for estimation of optimal parameters of the nonlinear Muskingum routing model and we used sum of squared error criteria to evaluate that. The results indicated that continuous ant colony algorithm has good efficiency on Wilson flood and Wye River flood with SSE=36.7679 and 37944.14. After ensuring the efficiency of ACOr algorithm, we used nonlinear Muskingum routing model investigated for Karoon River and the value of SSE was equal to 144691.735 that indicated higher performance in comparing to bee colony algorithm (SEE = 177161.4). Also, in this study the three flood routing have investigated by the convex method. This method didn’t has good performance on Wilson flood and Wye River flood but in Karoon River flood routing indicated better performance than nonlinear Muskingum methods. This model provided good estimation of peak of flow discharge, that this issue is very important for Implementation of flood warning systems.