Linear Muskingum Flood Routing Method in Branched Rivers Using Genetic Algorithm Optimization Technique

Flood routing is considered to be one of the most significant issues in many water engineering projects. Hydraulic flood routing methods particularly in branched rivers and rivers with unknown intermediate basin are commonly used because of their high accuracy compared to hydrologic methods. These methods need providing cross sections and bed slopes at all reaches of the flow path which requires a lot of time and budget. On the other hand, hydrologic methods such as the Muskingum method are very simple and require much less data and time. The Muskingum method is one of the common hydrologic flood routing methods of rivers. In order to apply this method for a certain reach, two basic parameters have to be determined. These parameters are functions of the river specifications and usually are estimated by using trial and error combined with a curve fitting technique. The method is quite simple but it is restricted to single reach problems. In this study, a method is proposed in which the calibration (parameter estimation) is performed by using the genetic algorithm, which is capable of solving complicated problems including more parameters. The proposed method is applied for single and multiple reach problems in addition to a river with lacking data of the intermediate catchment hydrograph. In all applications, comparisons of observed and computed outflow hydrographs indicate satisfactory agreements.