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.

洪水是破坏力最大的自然灾害之一，它极大地损害了人们的生活。因此，研究人员和政客研究洪水泛滥至关重要。工程师和研究人员在洪水路由中已经考虑了非线性Muskingum模型。在这项研究中，为了提高流出量预测的准确性，首次提出了具有四个可变参数的新型非线性马斯金格模型。在提出的模型中，入流被分成三个子区域，并且四个水文参数中的每个在每个子区域中具有不同的值。通过结合粒子群优化和遗传算法来确定如何选择子区域以及水文参数的值。在四个案例研究中对提出的模型进行了研究。与具有三个参数的非线性Muskingum模型相比，第一和第二个案例研究的平方和偏差（SSQ）分别减少了52％和6.9％。与最佳可变参数模型相比，第三和第四种案例研究的SSQ分别降低了76％和62％。结果表明，在所有四个案例研究中，SSQ均显着降低，并且所提出的模型优于迄今为止其他研究人员使用的其他非线性Muskingum模型。