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基于抗差算法的马斯京根参数估计

  • 赵超
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  • 1. 厦门理工学院水资源环境研究所, 厦门 361005;
    2. 近海海洋环境科学国家重点实验室、厦门大学环科中心, 厦门 361005

收稿日期: 2009-06-30

  修回日期: 2010-02-03

  网络出版日期: 2010-07-15

基金资助

Supported by the National Natural Science Foundation (50909084),the Natural Science Foundation of Fujian Province(2009J05107),and Xiamen University of Technology (YKJ08015R) 

Brief Report Parameter estimation for Muskingum routing model based on robust algorithm

  • ZHAO Chao
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  • 1. Water Resources and Environmental Institute, Xiamen University of Technology, Xiamen 361005, China;
    2. State Key Laboratory of Marine Environmental Science, Environmental Science Research Center, Xiamen University, Xiamen 361005, China

Received date: 2009-06-30

  Revised date: 2010-02-03

  Online published: 2010-07-15

Supported by

Supported by the National Natural Science Foundation (50909084),the Natural Science Foundation of Fujian Province(2009J05107),and Xiamen University of Technology (YKJ08015R) 

摘要

用于估计马斯京根模型参数的方法很多,但这些方法在数据存在异常值时缺乏抵御异常值影响的抗差性能. 推导出一种有限制条件的参数抗差估计算法,通过含有随机误差和异常误差的人工数据和真实数据比较抗差算法与传统最小二乘算法的抗差性. 研究表明抗差估计算法能减小异常值对参数估值的影响.

本文引用格式

赵超 . 基于抗差算法的马斯京根参数估计[J]. 中国科学院大学学报, 2010 , 27(4) : 556 -562 . DOI: 10.7523/j.issn.2095-6134.2010.4.018

Abstract

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.

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