# Hilbert-Huang Transform Analysis Of Hydrological And by A. Ramachandra Rao, En-Ching Hsu (auth.)

By A. Ramachandra Rao, En-Ching Hsu (auth.)

The Hilbert-Huang remodel ((HHT) is a lately built process that is used to investigate nonstationary facts. Hydrologic and environmental sequence are, ordinarily, analyzed through the use of strategies that have been constructed for desk bound info. This has resulted in difficulties of interpretation of the consequences. Environmental and hydrologic sequence are mostly nonstationary. the elemental goal of the fabric mentioned during this publication is to investigate those facts by utilizing tools in line with the Hilbert-Huang remodel. those effects are in comparison to the implications from the conventional equipment resembling these in keeping with Fourier remodel and different classical statistical tests.

Audience
This e-book may be of price to researchers attracted to weather swap and complicated graduate scholars in civil engineering, atmospheric sciences and statistics.

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Sample text

Obviously, for a stationary process, the Hilbert spectrum cannot be a function of time; in such a case the Hilbert spectrum only contains horizontal lines when plotted against . For a pure stationary case, the DS will then be identically zero. Only under this condition, marginal Hilbert spectrum will be identical to Fourier spectrum and then Fourier spectrum makes physical sense. 2) where the overline indicates averaging over a definite but shorter time span, T , than the overall time duration of the data, T .

For comparison, Fourier, Multitaper and marginal Hilbert spectra are computed. Histograms of the data in time or spatial domain help us to examine the distribution of the simulated signal compared to the original signal. Autocorrelation function is used to compare the persistence of simulated and the original data. The autocorrelation functions (Box and Jenkins, 1976) yn at are used to detect non-randomness in data. For given measurements, y1 y2 t=1 2 n, the lag k autocorrelation function is defined in Eq.

Autocorrelation function is used to compare the persistence of simulated and the original data. The autocorrelation functions (Box and Jenkins, 1976) yn at are used to detect non-randomness in data. For given measurements, y1 y2 t=1 2 n, the lag k autocorrelation function is defined in Eq. 8). 8) yi − y¯ 2 i=1 In this section, method one, which is simulated only with random phase, is examined by using several sets of data. For different types of data, the results from one or two samples are used for demonstration.