Flood Routing: Improving Outflow Using a New Non-linear Muskingum Model with Four Variable Parameters Coupled with PSO-GA Algorithm

Abstract

Flood is one of the most destructive natural disasters that damages people’s lives dramatically. Thus, it is crucial for researchers and politicians to research flood routing. The non-linear Muskingum model has been significantly considered by engineers and researchers in flood routing. In this study, in order to increase the accuracy of outflow prediction, the new non-linear Muskingum model, with four variable parameters, is proposed for the first time. In the proposed model, the inflows are divided into three sub-regions, and each of the four hydrologic parameters has a various value in each sub-region. How to select the sub-regions, as well as the values of the hydrologic parameters, is determined by combining both the Particle Swarm Optimization and Genetic Algorithm. The proposed model is studied in four case studies. Compared to the non-linear Muskingum model with three parameters, the amount of sum squared deviation (SSQ) decreased 52 and 6.9% for the first and second case studies, respectively. Compared to the best variable parameter model, the SSQ for the third and fourth case studies reduced 76 and 62%, respectively. The results showed that the SSQ was considerably decreased significantly in all of the four case studies, and the proposed model has superiority over other non-linear Muskingum models, which have been used by other researchers so far.