scholarly journals Trends analysis of precipitation data over the tropical South-West Indian Ocean (SWIO) basin using the Ensemble Empirical Mode Decomposition (EEMD) method

MAUSAM ◽  
2021 ◽  
Vol 67 (2) ◽  
pp. 423-430
Author(s):  
K. BOODHOO ◽  
M. R. LOLLCHUND ◽  
A. F. DILMAHAMOD
Keyword(s):  
Time Series ◽  
Indian Ocean ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
South West Indian Ocean ◽  
South West ◽  
Mode Decomposition ◽  
West Indian Ocean ◽  
Eemd Method

In this paper, we propose the use of the Ensemble Empirical Mode Decomposition (EEMD) method in the analysis of trends in climate data. As compared to existing traditional methods, EEMD is simple, fast and reliable. It works by decomposing the time-series data into intrinsic mode functions until a residual component is obtained which represents the trend in the data. The dataset considered consists of satellite precipitation estimates (SPE) obtained from the Tropical Rainfall Measuring Mission (TRMM) for the tropical South-West Indian Ocean (SWIO) basin recorded during the periods January 1998 to December 2013. The SWIO basin spans from the latitudes 5° S to 35° S and the longitudes 30° E to 70° E and comprises of part of the east coast of Africa and some small island developing states (SIDS) such as Comoros, Madagascar, Mauritius and Reunion Island. The EEMD analysis is carried out for summer, winter and yearly time series of the SPE data. The results from the study are presented in terms of intrinsic mode functions (IMFs) and the trends. The analysis reveals that in summer, there is a tendency to have an increase in the amount of rainfall, whereas in winter, from 1998 to 2004 there has been an initial increase of 0.0022 mm/hr/year and from there onwards till 2013 a decrease of 0.00052 mm/hr/year was noted.  

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

2021 ◽  
Author(s):  
Chun-Hsiang Tang ◽  
Christina W. Tsai
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Time Frequency ◽  
Spatial Locality ◽  
Temperature And Precipitation ◽  
Mode Decomposition ◽  
Climatic Scenarios

<p>Abstract</p><p>Most of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.</p><p>In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.</p><p> </p><p>Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought</p>

Random Noise Reduction Based on Ensemble Empirical Mode Decomposition and Wavelet Threshold Filtering

2012 ◽  
Vol 518-523 ◽  
pp. 3887-3890 ◽  
Author(s):  
Wei Chen ◽  
Shang Xu Wang ◽  
Xiao Yu Chuai ◽  
Zhen Zhang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Reduction Method ◽  
Random Noise ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Eemd Method

This paper presents a random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, we have conducted spectrum analysis and analyzed the frequency band range of effective signals and noise. Secondly, we make use of EEMD method on seismic signals to obtain intrinsic mode functions (IMFs) of each trace. Then, wavelet threshold noise reduction method is used on the high frequency IMFs of each trace to obtain new high frequency IMFs. Finally, reconstruct the desired signal by adding the new high frequency IMFs on the low frequency IMFs and the trend item together. When applying our method on synthetic seismic record and field data we can get good results.

scholarly journals Potato price analysis of Delhi market through ensemble empirical mode decomposition

2019 ◽  
Vol 34 (01) ◽  
Author(s):  
Kapil Choudhary ◽  
Girish Kumar Jha ◽  
Rajeev Ranjan Kumar
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Agricultural Commodities ◽  
Intrinsic Mode Functions ◽  
Price Series ◽  
Mode Decomposition ◽  
Non Linear ◽  
Agricultural Price ◽  
Commodities Prices

Agricultural commodities prices depends on production, unnecessary demand, production uncertainty, market flaws etc. Due to these factors agricultural price series are non-stationary and non-linear in nature. Therefore analyzing agricultural commodities prices is considered as a challenging task. The traditional stationary approach of time series is unable to capture non-stationary and non-linear properties of agricultural price series. Non-stationary and non-linear properties present in the price series may be accurately analyzed through empirical mode decongation (EMD). In this technique, the original time series decomposed into intrinsic mode functions and residue. One of the major limitation of EMD is the presence of the mode mixing. To overcome this limitation of the EMD, we use ensemble empirical mode decomposition (EEMD). Using this technique in this study, Delhi market potato prices have been analyzed.

scholarly journals The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1666 ◽  
Author(s):  
Neeraj Bokde ◽  
Andrés Feijóo ◽  
Nadhir Al-Ansari ◽  
Siyu Tao ◽  
Zaher Mundher Yaseen
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Arima Model ◽  
Ensemble Empirical Mode Decomposition ◽  
Power Modeling ◽  
Short Term ◽  
Mode Decomposition ◽  
Eemd Method

In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.

FUNDAMENTAL MODES OF ATMOSPHERIC CFC-11 FROM EMPIRICAL MODE DECOMPOSITION

2012 ◽  
Vol 04 (04) ◽  
pp. 1250024 ◽  
Author(s):  
KOSEKI J. KOBAYASHI-KIRSCHVINK ◽  
KING-FAI LI ◽  
RUN-LIE SHIA ◽  
YUK L. YUNG
Keyword(s):  
Empirical Mode Decomposition ◽  
Annual Cycle ◽  
Fourier Transforms ◽  
Ensemble Empirical Mode Decomposition ◽  
Polar Regions ◽  
Initial Growth ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Legislative Control ◽  
Eemd Method

Following an initial growth, the concentrations of chlorofluorocarbon-11 (CFC-11) in the atmosphere started to decline in the 1990's due to world-wide legislative control on emissions. The amplitude of the annual cycle of CFC-11 was much larger in the earlier period compared with that in the later period. We apply here the Ensemble Empirical Mode Decomposition (EEMD) analysis to the CFC-11 data obtained by the U.S. National Oceanic and Atmospheric Administration. The sum of the second and third intrinsic mode functions (IMFs) represents the annual cycle, which shows that the annual cycle of CFC-11 has varied by a factor of 2–3 from the mid-1970's to the present over polar regions. The results provide an illustration of the power of the EEMD method in extracting a variable annual cycle from data dominated by increasing and decreasing trends. Finally, we compare the annual cycle obtained by the EEMD analysis to that obtained using conventional methods such as Fourier transforms and running averages.

The Fault Pattern Identification of Hydraulic Pump Based on the Sensitive Component STSA

2014 ◽  
Vol 635-637 ◽  
pp. 790-794
Author(s):  
Yu Kui Wang ◽  
Hong Ru Li ◽  
Peng Ye
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hydraulic Pump ◽  
Mode Decomposition ◽  
Novel Method ◽  
Symbolic Time Series Analysis

A novel method which is based on ensemble empirical mode decomposition (EEMD) and symbolic time series analysis (STSA) was proposed in this paper. Firstly, the vibration signal of hydraulic pump was decomposed into a number of stationary intrinsic mode functions (IMFs). Secondly, the sensitive component was extracted. Finally, the relative entropy (RE) was extracted from the sensitive components and they were used as the indicator to distinguish the faults of hydraulic pump. The research results of actual testing vibration signal demonstrated the rationality and effectiveness of the proposed method in this paper.

scholarly journals The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xiwen Qin ◽  
Qiaoling Li ◽  
Xiaogang Dong ◽  
Siqi Lv
Keyword(s):  
Random Forest ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Normal Operation ◽  
Intrinsic Mode Functions ◽  
Wavelet Method ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Important Significance

Accurate diagnosis of rolling bearing fault on the normal operation of machinery and equipment has a very important significance. A method combining Ensemble Empirical Mode Decomposition (EEMD) and Random Forest (RF) is proposed. Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by EEMD, and the effective IMFs are selected. Then their energy entropy is calculated as the feature. Finally, the classification is performed by RF. In addition, the wavelet method is also used in the proposed process, the same as EEMD. The results of the comparison show that the EEMD method is more accurate than the wavelet method.

scholarly journals ReNovRisk: a multidisciplinary programme to study the cyclonic risks in the South-West Indian Ocean

2021 ◽  
Author(s):  
Pierre Tulet ◽  
Bertrand Aunay ◽  
Guilhem Barruol ◽  
Christelle Barthe ◽  
Remi Belon ◽  
...  
Keyword(s):  
Indian Ocean ◽  
West Indian ◽  
Ocean Basin ◽  
Transport Processes ◽  
The South ◽  
South West Indian Ocean ◽  
South West ◽  
The Face ◽  
West Indian Ocean ◽  
Solid Transport

AbstractToday, resilience in the face of cyclone risks has become a crucial issue for our societies. With climate change, the risk of strong cyclones occurring is expected to intensify significantly and to impact the way of life in many countries. To meet some of the associated challenges, the interdisciplinary ReNovRisk programme aims to study tropical cyclones and their impacts on the South-West Indian Ocean basin. This article is a presentation of the ReNovRisk programme, which is divided into four areas: study of cyclonic hazards, study of erosion and solid transport processes, study of water transfer and swell impacts on the coast, and studies of socio-economic impacts. The first transdisciplinary results of the programme are presented together with the database, which will be open access from mid-2021.

scholarly journals Diversity and Geographical Structure of Xanthomonas citri pv. citri on Citrus in the South West Indian Ocean Region

2021 ◽  
Vol 9 (5) ◽  
pp. 945
Author(s):  
Olivier Pruvost ◽  
Damien Richard ◽  
Karine Boyer ◽  
Stéphanie Javegny ◽  
Claudine Boyer ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Host Range ◽  
Disease Surveillance ◽  
West Indian ◽  
Copper Resistance ◽  
The South ◽  
South West Indian Ocean ◽  
South West ◽  
Strain Collection ◽  
West Indian Ocean

A thorough knowledge of genotypic and phenotypic variations (e.g., virulence, resistance to antimicrobial compounds) in bacteria causing plant disease outbreaks is key for optimizing disease surveillance and management. Using a comprehensive strain collection, tandem repeat-based genotyping techniques and pathogenicity assays, we characterized the diversity of X. citri pv. citri from the South West Indian Ocean (SWIO) region. Most strains belonged to the prevalent lineage 1 pathotype A that has a wide host range among rutaceous species. We report the first occurrence of genetically unrelated, nonepidemic lineage 4 pathotype A* (strains with a host range restricted to Mexican lime and related species) in Mauritius, Moheli and Réunion. Microsatellite data revealed that strains from the Seychelles were diverse, grouped in three different clusters not detected in the Comoros and the Mascarenes. Pathogenicity data suggested a higher aggressiveness of strains of one of these clusters on citron (Citrus medica). With the noticeable exception of the Comoros, there was no sign of recent interisland movement of the pathogen. Consistent with this finding, the copL gene, a marker for the plasmid-borne copLAB copper resistance that was recently identified in Réunion, was not detected in 568 strains from any islands in the SWIO region apart from Réunion.

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