The Research Of Parameters And The Optimal Parameters’ Estimation Method Of Muskingum Model For River Flood Routing

In this paper, as Baise-Tiandong river for the research object, in order to study influence of the size of the magnitude of the flood river on Muskingum model parameters, five unimodal type flood process data of the peak flow magnitude increasing in turn are chosen from the historical observed flood data in the Baise hydrological (3) station.Due to Some tributaries without hydrological station in Baise-Tiandong interval, so the Arcgis10.0 software is combined, Three-water-source xin’anjiang model is used to forecast interval inflow of Muskingum model.For five flood, the interzone inflow correction considered, first of all, the traditional method of trial and error is used to solve the Muskingum model parameters, then according to the obtained parameters K and x for this flood routing, with interzone inflow linear superposition, error sum of squares and correlation coefficient of calculated discharge and measured discharge in Tiandong station calculated. The calculation results show that the error sum of squares is larger and the correlation coefficient is low; It shows:in guarantee channel storage capacity and in store traffic recently as in a straight line, not necessarily can make the calculated discharge and measured discharge to minimize the error sum of squares. Then, to solve by the nonlinear programming, it is concluded that the error sum of squares than trial and error method reduce bigger, the correlation coefficient increasing. It suggests that the method using the error sum of squares minimum of calculated discharge and measured discharge as the criterion to optimize flow routing coefficient c0, c1 and c2, then reverse K and x, is feasible, but it is not theoretical basis for the global optimal solution; Finally, with the swarm algorithm of calculation simple and the control parameters less, good convergence, strong robustness, strong global search ability and other characteristics to optimize Muskingum model parameters:start with a nonlinear function to verify the extremal optimization ability of swarm algorithm, then used to obtain optimal solution, the results show that the error sum of squares of calculated discharge and measured discharge compared with the nonlinear programming method reduce in Tiandong station, the correlation coefficient increasing. And then to test the superiority of the algorithm, comparing with the commonly used particle swarm algorithm optimizing the results, Swarm algorithm, optimization to get the error sum of squares slightly lower, correlation coefficient slightly larger than particle swarm optimization. But under the same number of iterations, the swarm algorithm converges faster, more close to the global optimal value.So the Muskingum model parameters optimization based on swarm algorithm has higher accuracy and scientificity. Swarm algorithm optimization results can be used as Muskingum model parameter values of the Baise- Tiandong river, that method optimization shows:with the increase of the magnitude of the peak flow of five flood in Baise hydrological station, K values reduce bigger accordingly, so to carry on optimal fitting of the Baise station’s flood peak flow and K. In the future, according to the fitting formula to calculate the corresponding K value through the peak flow in the river flood routing is suggest, and x values change relatively stable, so average value 0.24 of the five flood calculated results can be used as value x.