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首页> 外文期刊>Journal of Hydrology >Use of universal function approximation in variance-dependent surface interpolation method: An application in hydrology
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Use of universal function approximation in variance-dependent surface interpolation method: An application in hydrology

机译：泛函近似在依赖方差的表面插值方法中的应用：在水文学中的应用

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摘要

Variance dependent stochastic interpolation approaches such as kriging are widely recognized as standard stochastic methods for interpolation of geophysical and hydrologic variables. Deterministic weighting and stochastic interpolation methods are the most frequently used methods for estimating missing rainfall values at a gage based on values recorded at all other available recording gages. Traditional kriging has a major limitation due to the need for an a priori definition of a mathematical function for a semivariogram that might fit the surface to be interpolated. Use of the universal function approximator, artificial neural network (ANN), as a replacement to fitted authorized semivariogram model within ordinary kriging is investigated in the current study. The revised ordinary kriging is used for estimation of missing precipitation data at a rainfall gaging station based on data recorded at all other available gaging stations. Historical daily precipitation data obtained from 15 rain gaging stations from a temperate climatic region, Kentucky, USA, is used to test the improvised method and derive conclusions about the efficacy of this method. Results suggest that use of universal function approximator such as ANN within a kriging has several advantages over ordinary kriging. (c) 2006 Elsevier B.V. All rights reserved.
机译：像克里金法这样的方差相关随机插值方法被广泛认为是地球物理和水文变量插值的标准随机方法。确定性加权和随机插值方法是最常用的方法，用于基于在所有其他可用记录仪上记录的值来估算一个仪仪上的降雨缺失值。由于需要对可能适合要插值曲面的半变异函数的数学函数进行先验定义，因此传统的克里金法存在很大的局限性。在本研究中，研究了使用通用函数逼近器，人工神经网络（ANN）替代普通克里金法中拟合授权的半变异函数模型。修改后的普通克里格法用于基于在所有其他可用测量站上记录的数据来估算降雨测量站上缺少的降水数据。从美国肯塔基州的一个温带气候区的15个雨量站获得的历史日降水量数据被用于测试该简易方法并得出有关该方法有效性的结论。结果表明，在通用克里格内使用通用函数逼近器（例如ANN）比普通克里格具有多个优势。 （c）2006 Elsevier B.V.保留所有权利。

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