Brief Report Parameter estimation for Muskingum routing model based on robust algorithm
基于抗差算法的马斯京根参数估计

There are a variety of techniques for estimating the parameters of the Muskingum routing model. However the robustness of these methods has to be questioned because of the tendency of outliers in data to strongly influence the outcome. A robust estimation has been presented. The robustness of this estimator has been compared with the least squares method by means of synthetic data sets, in which both Gaussian random errors and outliers have been introduced. The study demonstrates that the robust estimator has the potential to reduce estimation bias in the presence of outliers, and it has an advantage over the least squares method.