scholarly journals Determination of variation uncertainty in runoff time series at multi-temporal scales

2021 ◽  
Author(s):  
Yan Ye ◽  
Jinping Zhang ◽  
Xunjian Long ◽  
Lihua Ma ◽  
Yong Ye
Keyword(s):  
Time Series ◽  
Annual Runoff ◽  
Important Variable ◽  
Temporal Scales ◽  
Flood Season ◽  
Mode Function ◽  
Mode Decomposition ◽  
Multi Temporal ◽  
Emd Method

Abstract In order to survey the possible periodic, uncertainty and common features in runoff with multi-temporal scales, the empirical mode decomposition (EMD) method combined with the set pair analysis (SPA) method was applied, with data observed at Zhangjiashan hydrological station. The results showed that the flood season and annual runoff time series consisted of four intrinsic mode function (IMF) components, and the non-flood season time series exhibited three IMF components. Moreover, based on the different coupled set pairs from the time series, the identity, discrepancy, and contrary of different periods at multi-temporal scales were determined by the SPA method. The degree of connection μ between the flood season and annual runoff periods were the highest, with 0.94, 0.77, 0.7 and 0.73, respectively, and the μ between the flood periods and the non-flood periods were the lowest, with 0.66, 0.46, 0.24 and 0.24, respectively. Third, the maximum μ of each SPA appeared in the first mode function. In general, the different extractive periods decomposed by EMD method can reflected the average state of Jinghe River. Results also verified that runoff suffered from seasonal and periodic fluctuations, and fluctuations in the short-term corresponded to the most important variable. Therefore, the conclusions draw in this study can improve water resources regulation and planning.

Application of MEEMD-ARIMA combining model for annual runoff prediction in the Lower Yellow River

2019 ◽  
Vol 11 (3) ◽  
pp. 865-876 ◽  
Author(s):  
Xianqi Zhang ◽  
Wei Tuo ◽  
Chao Song
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Yellow River ◽  
Arima Model ◽  
Ensemble Empirical Mode Decomposition ◽  
Annual Runoff ◽  
Lower Yellow River ◽  
The Lower Yellow River ◽  
Mode Decomposition ◽  
Runoff Prediction

Abstract The prediction of annual runoff in the Lower Yellow River can provide an important theoretical basis for effective reservoir management, flood control and disaster reduction, river and beach management, rational utilization of regional water and sediment resources. To solve this problem and improve the prediction accuracy, permutation entropy (PE) was used to extract the pseudo-components of modified ensemble empirical mode decomposition (MEEMD) to decompose time series to reduce the non-stationarity of time series. However, the pseudo-component was disordered and difficult to predict, therefore, the pseudo-component was decomposed by ensemble empirical mode decomposition (EEMD). Then, intrinsic mode functions (IMFs) and trend were predicted by autoregressive integrated moving average (ARIMA) which has strong ability of approximation to stationary series. A new coupling model based on MEEMD-ARIMA was constructed and applied to runoff prediction in the Lower Yellow River. The results showed that the model had higher accuracy and was superior to the CEEMD-ARIMA model or EEMD-ARIMA model. Therefore, it can provide a new idea and method for annual runoff prediction.

scholarly journals CHARACTERIZATION OF LAND COVER SEASONALITY IN SENTINEL-1 TIME SERIES DATA

2020 ◽  
Vol V-3-2020 ◽  
pp. 97-104
Author(s):  
C. Dubois ◽  
M. M. Mueller ◽  
C. Pathe ◽  
T. Jagdhuber ◽  
F. Cremer ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Coniferous Forest ◽  
Series Data ◽  
Urban Land Cover ◽  
Multi Temporal ◽  
Forested Areas

Abstract. In this study, we analyze Sentinel-1 time series data to characterize the observed seasonality of different land cover classes in eastern Thuringia, Germany and to identify multi-temporal metrics for their classification. We assess the influence of different polarizations and different pass directions on the multi-temporal backscatter profile. The novelty of this approach is the determination of phenological parameters, based on a tool that has been originally developed for optical imagery. Furthermore, several additional multitemporal metrics are determined for the different classes, in order to investigate their separability for potential multi-temporal classification schemes. The results of the study show a seasonality for vegetation classes, which differs depending on the considered class: whereas pastures and broad-leaved forests show a decrease of the backscatter in VH polarization during summer, an increase of the backscatter in VH polarization is observed for coniferous forest. The observed seasonality is discussed together with meteorological information (precipitation and air temperature). Furthermore, a dependence of the backscatter of the pass direction (ascending/descending) is observed particularly for the urban land cover classes. Multi-temporal metrics indicate a good separability of principal land cover classes such as urban, agricultural and forested areas, but further investigation and use of seasonal parameters is needed for a distinct separation of specific forest sub-classes such as coniferous and deciduous.

Two Solutions of EMD Mode Mixing

2012 ◽  
Vol 433-440 ◽  
pp. 5364-5367
Author(s):  
Xue Ming Zhai ◽  
Li Na Zhao ◽  
Pan Zhang
Keyword(s):  
Signal Analysis ◽  
High Amplitude ◽  
Sampling Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Frequency Components ◽  
Mixing Problem ◽  
Emd Method ◽  
Mode Mixing

Based on the mode mixing problem which exists in EMD(Empirical Mode Decomposition)method, this paper provides two ways to solve this problem.First is to increase the sampling frequency, add a characteristic wave with high amplitude and frequency in the process of signal analysis,then put the FFT transformation on the IMF(intrinsic mode function) which derives from EMD method,while in the process of transformation,binary scaling to the sampling frequency to adapt to the changes of each IMF component, making the spectrum more clear and the analysis of the data with the relevant frequency components more careful.The second is EEMD ,which is proposed in recent years.We use this method to deal with the signal ,after that,then use EMD method to analyse it,which will get a good result.While a comparison with the two methods and point out the different ranges of applications.

A Simplified Method of Trend Removal to Determine Noise Observed During a Supercapacitor’s Discharging

2019 ◽  
Vol 18 (02) ◽  
pp. 1940001 ◽  
Author(s):  
Ł. Lentka ◽  
J. Smulko
Keyword(s):  
Time Series ◽  
Polynomial Approximation ◽  
Additive Noise ◽  
Statistical Parameters ◽  
Noise Component ◽  
Efficient Detection ◽  
Mode Decomposition ◽  
Simplified Method ◽  
Trend Removal

In this paper, new method of trend removal is proposed. This is a simplified method based on Empirical Mode Decomposition (EMD). The method was applied for voltage time series observed during supercapacitor discharging process. It assured the determination of an additive noise component after subtracting the identified trend component. We analyzed voltage time series observed between the terminals of the supercapacitor when discharged by a loading resistance [Formula: see text]. The steps of the proposed method are presented in detail. The results are compared with the results obtained for polynomial approximation. Statistical parameters (kurtosis, skewness) of the histograms of the identified noise component were estimated to evaluate the quality of the proposed detrending method. The method was adjusted to the analyzed data by selecting a parameter of the applied envelope function of the EMD method. We conclude that the proposed method is faster and more efficient for detecting the additive noise component than the competitive polynomial approximation. The identified noise component may be used to evaluate the State of Health of tested supercapacitors and therefore requires fast algorithms with efficient detection.

scholarly journals Using empirical mode decomposition to correlate paleoclimatic time-series

2007 ◽  
Vol 7 (2) ◽  
pp. 299-307 ◽  
Author(s):  
J. Solé ◽  
A. Turiel ◽  
J. E. Llebot
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Mode Decomposition ◽  
Sampling Rate ◽  
Temporal Correlation ◽  
Mode Decomposition ◽  
Milankovich Cycles ◽  
Forcing Mechanisms ◽  
Phase Differences

Abstract. Determination of the timing and duration of paleoclimatic events is a challenging task. Classical techniques for time-series analysis rely too strongly on having a constant sampling rate, which poorly adapts to the uneven time recording of paleoclimatic variables; new, more flexible methods issued from Non-Linear Physics are hence required. In this paper, we have used Huang's Empirical Mode Decomposition (EMD) for the analysis of paleoclimatic series. We have studied three different time series of temperature proxies, characterizing oscillation patterns by using EMD. To measure the degree of temporal correlation of two variables, we have developed a method that relates couples of modes from different series by calculating the instantaneous phase differences among the associated modes. We observed that when two modes exhibited a constant phase difference, their frequencies were nearly equal to that of Milankovich cycles. Our results show that EMD is a good methodology not only for synchronization of different records but also for determination of the different local frequencies in each time series. Some of the obtained modes may be interpreted as the result of global forcing mechanisms.

scholarly journals Elastic-Net Regression based on Empirical Mode Decomposition for Multivariate Predictors

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Abdullah Suleiman Al-Jawarneh ◽  
Mohd. Tahir Ismail
Keyword(s):  
Empirical Mode Decomposition ◽  
Real Data ◽  
Elastic Net ◽  
Predictor Variables ◽  
Response Variable ◽  
Mode Function ◽  
Mode Decomposition ◽  
Finite Set ◽  
Nonlinear Signal ◽  
Emd Method

The empirical mode decomposition (EMD) method is used to decompose the non-stationary and nonlinear signal into a finite set of orthogonal non-overlapping time scale components that include several intrinsic mode function components and one residual component. Elastic net (ELN) regression is a statistical penalized method used to address multicollinearity among predictor variables and identify the necessary variables that have the most effect on the response variable. This study proposed the use of the ELN method based on the EMD algorithm to identify the decomposition components of multivariate predictor variables with the most effect on the response variable under multicollinearity problems. The results of the numerical experiments and real data confirmed that the EMD-ELN method is highly capable of identifying the decomposition components with the presence or absence of multicollinearity among the components. The proposed method also achieved the best estimation and reached the optimal balance between the variance and bias. The EMD-ELN method also improved the accuracy of regression modeling compared with the traditional regression models.

scholarly journals Multi-temporal runoff-sediment discharge relationships

2022 ◽  
Author(s):  
Honglin Xiao ◽  
Jinping Zhang ◽  
Hongyuan Fang
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Annual Runoff ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Sediment Discharge ◽  
Discharge Data ◽  
Temporal Correlations ◽  
Mode Decomposition ◽  
Multi Temporal ◽  
Runoff Sediment

To understand the runoff-sediment discharge relationship , this study examined the annual runoff and sediment discharge data obtained from the Tangnaihai hydrometric station. The data were decomposed into multiple time scales through Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN). Furthermore, double cumulative curves were plotted and the cointegration theory was employed to analyze the microscopic and macroscopic multi-temporal correlations between the runoff and the sediment discharge and their detailed evolution.

Periodic Change of River Runoff Based on EMD-MES

2011 ◽  
Vol 250-253 ◽  
pp. 2848-2851
Author(s):  
Xue Hua Zhao ◽  
Li Li An
Keyword(s):  
Time Series ◽  
Maximum Entropy ◽  
Yellow River ◽  
Annual Runoff ◽  
Scientific Data ◽  
Entropy Spectrum ◽  
Mode Decomposition ◽  
Periodic Analysis ◽  
Runoff Series ◽  
Maximum Entropy Spectrum

This paper discusses stabilizing treatment of runoff time series by empirical mode decomposition (EMD), and periodic analysis of stabilized runoff time series by maximum entropy spectrum, and presents high-resolution character of maximum entropy spectrum and its application prospect in hydrology. It conducts the analysis and calculation in combination with a real example of annual runoff series at the Lanzhou station in the upper of Yellow River, and study proves that annual runoff has 11.1, 6.25 and 3.1 years significant periods at the Lanzhou station. The conclusion illustrates the feasibility of this method and provides scientific data for water resources planning and managing.

Physiological Time Series Processing via Empirical Wavelet Transform

2020 ◽  
Vol 12 (5) ◽  
pp. 582-587
Author(s):  
Omkar Singh
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Multivariate Data ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Temporal Scales ◽  
Physiological Time ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform

This paper presents the efficacy of empirical wavelet transform (EWT) for physiological time series processing. At first, EWT is applied to multivariate heterogeneous physiological time series. Secondly, EWT is used for the removal of fast temporal scales in multiscale entropy analysis. Empirical mode decomposition is an adaptive data analysis method in the sense that it does not require prior information about the signal statistics and tend to decompose a signal into various constituent modes. The utility of Standard EMD algorithm is however limited to single channel data as it suffers from the problems of mode alignment and mode mixing when applied channel wise for multivariate data. The standard EMD algorithm was extended to multivariate Empirical mode decomposition (MEMD) that can be used analyze a multivariate data. The MEMD can only be applied to multivariate data in which all the channels have equal data length. EWT is another adaptive technique for mode extraction in a signal using empirical scaling and wavelet functions. The multiscale entropy (MSE) algorithm is generally used to quantify the complexity of a time series. The original MSE approach utilizes a coarse-graining process for the removal of fast temporal scales in a time series which is equivalent to applying a finite impulse response (FIR) moving average filter. In Refined Multiscale entropy (RMSE), the FIR filter was replaced with a low pass Butterworth filter which exhibits a better frequency response than that of a FIR filter. In this paper we have presented a new approach for the removal of fast temporal scales based on empirical wavelet transform. The empirical wavelet transform is also used as an innovative filtering approach in multiscale entropy analysis.

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