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Imputation of missing values in a precipitation-runoff process database

机译:在降水径流过程数据库中估算缺失值

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摘要

Hydrologists are often faced with the problem of missing values in a precipitation-runoff process database to construct runoff prediction models. They tend to use simple and naive methods to deal with the problem of missing data. Thus far, the common practice has been to discard observations with missing values. In this paper, we present some statistically principled methods for gap filling and discuss the pros and cons of these methods. We employ and discuss imputations of missing values by means of self-organizing map (SOM), multilayer perceptron (MLP), multivariate nearest-neighbor (MNN), regularized expectation-maximization algorithm (REGEM) and multiple imputation (Ml) in the context of a precipitation-runoff process database in northern Iran in order to construct a serially complete database for analyses such as runoff prediction. In our case, the SOM and MNN tend to give similar and robust results. REGEM and Ml build on the assumption of multivariate normal data, which we don't seem to have in one of our cases. MLP tends to produce inferior results because it fragments the data into 68 different models. Therefore, we conclude that it makes most sense to use either the computationally simple MNN method or the more demanding SOM.
机译:水文学家经常面临降水径流过程数据库中缺失值以构建径流预测模型的问题。他们倾向于使用简单而幼稚的方法来处理数据丢失的问题。迄今为止,通常的做法是丢弃具有缺失值的观测值。在本文中,我们提出了一些统计上原则上的缺口填补方法,并讨论了这些方法的优缺点。我们通过上下文中的自组织映射(SOM),多层感知器(MLP),多元最近邻(MNN),正则化期望最大化算法(REGEM)和多重插补(M1)来采用和讨论缺失值的插补在伊朗北部建立降水径流过程数据库,以建立一个序列完整的数据库来进行径流预测等分析。在我们的案例中,SOM和MNN往往会给出相似且可靠的结果。 REGEM和M1建立在多元正态数据的假设之上,在我们的一种情况下,我们似乎没有这种假设。 MLP往往会产生较差的结果,因为它将数据分成68个不同的模型。因此,我们得出结论,使用计算简单的MNN方法或要求更高的SOM是最有意义的。

著录项

  • 来源
    《Hydrology research》 |2009年第4期|420-432|共13页
  • 作者单位

    Department of Water Resources Engineering, LTH, Lund University, PO Box 118, Lund S-22100, Sweden Department of Range and Watershed Management, Faculty of Natural Resources, University of Guilan, PO Box 1144, Sowmehe Sara, Guilan, Iran;

    Department of Water Resources Engineering, LTH, Lund University, PO Box 118, Lund S-22100, Sweden;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    data fill in; imputation methods: SOM; MLP; MNN; REGEM; Ml; missing values; serially complete data;

    机译:数据填写;插补方法:SOM;MLP;MNN;REGEM;Ml;缺失值;连续完成的数据;

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