Noise Reduction Approach in Chaotic Hydrologic Time Series Revisited

2001 ◽  
Vol 26 (4) ◽  
pp. 537-550 ◽  
Author(s):  
Amin Elshorbagy
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Hydrologic Time Series ◽  
Reduction Approach

Noise reduction in chaotic hydrologic time series: facts and doubts

2002 ◽  
Vol 256 (3-4) ◽  
pp. 147-165 ◽  
Author(s):  
A. Elshorbagy ◽  
S.P. Simonovic ◽  
U.S. Panu
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Hydrologic Time Series

An Application of Elman Networks In Treatment and Prediction of Hydrologic Time Series

2011 ◽  
Vol 9 (3) ◽  
pp. 148-156
Author(s):  
Leonardo G. Tampelini ◽  
Clodis Boscarioli ◽  
Sarajane M. Peres ◽  
Silvio C. Sampaio
Keyword(s):  
Time Series ◽  
Hydrologic Time Series

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

Nonlinear noise reduction of chaotic time series based on multidimensional recurrent LS-SVM

2008 ◽  
Vol 71 (16-18) ◽  
pp. 3675-3679 ◽  
Author(s):  
Jiancheng Sun ◽  
Chongxun Zheng ◽  
Yatong Zhou ◽  
Yaohui Bai ◽  
Jianguo Luo
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Chaotic Time Series ◽  
Nonlinear Noise

scholarly journals Noise reduction by recycling dynamically coupled time series

2011 ◽  
Vol 21 (4) ◽  
pp. 043110 ◽  
Author(s):  
M. Eugenia Mera ◽  
Manuel Morán
Keyword(s):  
Time Series ◽  
Noise Reduction

A systematic approach to noise reduction in chaotic hydrological time series

1999 ◽  
Vol 219 (3-4) ◽  
pp. 103-135 ◽  
Author(s):  
B. Sivakumar ◽  
K.-K. Phoon ◽  
S.-Y. Liong ◽  
C.-Y. Liaw
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Systematic Approach

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

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