Noise reduction in chaotic hydrologic time series: facts, and doubts

被引:70
|
作者
Elshorbagy, A [1 ]
Simonovic, SP
Panu, US
机构
[1] Univ Kentucky, Kentucky Water Res Inst, Lexington, KY 40506 USA
[2] Univ Western Ontario, Dept Civil & Environm Engn, London, ON N6A 5B9, Canada
[3] Univ Western Ontario, Inst Catastroph Loss Reduct, London, ON N6A 5B9, Canada
[4] Lakehead Univ, Dept Civil Engn, Thunder Bay, ON P7B 5E1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
chaos theory; noise reductions; artificial neural networks; nonlinear time series analysis;
D O I
10.1016/S0022-1694(01)00534-0
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The issues of noise reduction and the reliability of its application to hydrologic time series are discussed. First, the concepts of noise, its effect, and noise reduction are briefly presented. Second, a few published articles in hydrology are critically reviewed with regard to the application of noise reduction to hydrologic data. Third,: a case study of the English River, Ontario, Canada, is used to support the conclusions. It is found that the commonly used algorithm for noise reduction in hydrologic data might also remove a significant part of the original signal and introduce an artificial chaoticity to the data. It is recommended that current noise reduction algorithms should be applied with caution and used only for better estimation of chaotic invariants. The raw data should always be the basis for any further hydrologic analysis. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:147 / 165
页数:19
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