Information entropy theory based noise canceling method in hydrological sequence analysis

A technology of sequence analysis and hydrological time series, applied in open-air water source surveys, measuring devices, instruments, etc., can solve problems that do not conform to noise characteristics, and achieve the effect of improving authenticity, accuracy, and good applicability

Inactive Publication Date: 2009-12-02
NANJING UNIV
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Problems solved by technology

At present, the threshold selection methods mainly include: fixed threshold method (FT), Stein unbiased risk threshold method (SURE), maximin principle threshold method (MAXMIN), etc., but in practice each method has advantages and disadvantages, and threshold selection The results are not the same; (3) The noise components separated by these methods often have good autocorrelation, which does not conform to the characteristics of the noise itself

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  • Information entropy theory based noise canceling method in hydrological sequence analysis
  • Information entropy theory based noise canceling method in hydrological sequence analysis
  • Information entropy theory based noise canceling method in hydrological sequence analysis

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example 2

[0087] Taking the 54-year runoff series measured at the Lijin Station of the Yellow River as an example, the "bior3.5" wavelet function was selected, and the level 3 was used for discrete wavelet transform. The separated noise components are described by Gauss distribution and P-III type distribution respectively.

[0088] When Gauss distribution and P-III type distribution are used to describe the noise components, the first-level wavelet coefficient threshold optimization results are both 9.2 ( Figure 9 and Figure 10 ), and then perform threshold quantization processing and noise component separation on the high-frequency wavelet coefficients of the first and second layers. Figure 11 Among them, the upper curve is the measured annual runoff sequence at Lijin Station, the middle curve is the main sequence, and the lower part is the separated noise component. The separation results of the noise components and the analysis results of the eigenvalues ​​of each sequence (Tab...

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Abstract

The invention discloses an information entropy theory noise canceling method in hydrological sequence analysis, which comprises the following steps: firstly, selecting wavelet function and wavelet decomposition layer number according to the basic property of a hydrological time sequence to be analyzed, and then carrying out discrete wavelet transform for the hydrological sequence to acquire wavelet coefficients d(j,k) on different time scale levels; determining a wavelet coefficient threshold value of each layer by using the optimized entropy rule of the wavelet coefficient threshold value; and carrying out hard or soft threshold value quantizing treatment for the high-frequency wavelet coefficient of each layer, and then reconstructing the treated wavelet coefficient to acquire a reconstructed main sequence in a real measured hydrological sequence, wherein the difference of the really-measured hydrological sequence and the reconstructed main sequence is a noise component, namely the separation of the noise component is realized. Based on the information entropy theory and the wavelet noise canceling thought, the method establishes the optimized entropy rule of the wavelet coefficient threshold value so as to effectively separate the noise component in the hydrological sequence by using a wavelet analysis method, and improves the reality and accuracy of hydrological data.

Description

technical field [0001] The invention relates to a hydrological time series analysis method, in particular to a noise elimination method based on information entropy theory in hydrological series analysis. Background technique [0002] Uncertainty phenomenon is an undeniable and neglected objective existence in hydrology and water resources system, so it has been a very important research content in stochastic hydrology for a long time. Information entropy theory (such as POME (principle of maximumentropy), etc.) is an effective method to study and solve uncertainty problems. Since E.T. Jaynes (Jaynes ET. Information theory and statistical mechanics [J]. Phys. Rev., 1957, 106: 620-630; 108: 171-190.) first clearly proposed POME in 1957, many scholars at home and abroad have devoted themselves to Based on the application research of information entropy theory in hydrology and water resource science, remarkable achievements have been made. [0003] Hydrological time series is...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C13/00
Inventor 桑燕芳王栋吴吉春朱庆平王玲祝晓彬
Owner NANJING UNIV
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