Forecasting daily flow rate-based intelligent hybrid models combining wavelet and Hilbert–Huang transforms in the mediterranean basin in northern Algeria

Abda, Zaki; Chettih, Mohamed

doi:10.1007/s11600-018-0188-0

Cited by 12 publications

(6 citation statements)

References 49 publications

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“…(2) m j (t) = e max (t) + e min (t) 2 if m j (t) ≡ 0, the function h j (t) would be an IMF; if not, the process would continue and iterate until the criteria are obtained. Please consult Abda and Chettih (2018) for more detail about this stage. 5.…”

Section: Multiple şEn's Innovative Trend Methodologymentioning

confidence: 99%

“…It has made very significant progress compared in the area of hydrology. The application in the hydrology field has shown its success since the beginning of the last decade, in particular in the hydrological time series analysis and forecasting (e.g., Chen et al 2021;Zhang et al 2021;Shi et al 2021;Saraiva et al 2021;Abda et al 2021;Freire et al 2019;Abda and Chettih 2018;Reddy and Adarsh 2016;Huang et al 2014;. It is worth highlighting that although CWT could give information and solution for our requirements, DWT and HHT are more flexible.…”

Section: New Insights From the Proposed Hybrid Methodsmentioning

confidence: 99%

“…The team of Norden E. Huang of NASA first proposed it in 1998 (Huang 1998), intending to study climate-atmospheric data. The basic idea is to offer a flexible spectral approach, facilitate the reading, and extract data information (Abda and Chettih 2018). EMD is a nonlinear method of data analysis, where the stationarity assumptions are not required.…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

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Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

et al. 2022

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In this paper, the Innovative Trend Methodology (ITM) and their inspired approaches, i.e., Double (D-ITM) and Triple (T-ITM), were combined with Hilbert Huang transform (HHT) time frequency-based method. The new hybrid methods (i.e., ITM-HHT, D-ITM-HHT, and T-ITM-HHT) were proposed and compared to the DWT-based methods in order to recommend the best method. Three total annual rainfall time series from 1920 to 2011 were selected from three hydrological basins in Northern Algeria. The new combined models (ITM-HHT, D-ITM-HHT, and T-ITM-HHT) revealed that the 1950–1975 period has significant wet episodes followed by a long-term drought observed in the western region of Northern Algeria, while Northeastern Algeria presented a wet period since 2001. The proposed approaches successfully detected, in a visible manner, hidden trends presented in the signals, which proves that the removal of some modes of variability from the original rainfall signals can increase the accuracy of the used approaches.

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Section: Multiple şEn's Innovative Trend Methodologymentioning

confidence: 99%

Section: New Insights From the Proposed Hybrid Methodsmentioning

confidence: 99%

Section: Empirical Mode Decompositionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

et al. 2022

Self Cite

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show abstract

“…By decomposing the complex time series data into intrinsic mode functions (IMFs) with trend characteristics and estimating the amplitude and frequency of IMFs, HHT can be used to obtain more information about the features of the time series fluctuations than basic statistics such as the mean, standard deviation, and skewness [ 26 ]. Because of the advantages of HHT in analyzing non-stationary and non-linear time series data, this method is widely used in various professional fields, such as geophysics, health monitoring, ocean engineering, chemical engineering, and financial analysis [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. HHT has also been thoroughly applied in atmospheric turbulence and meteorological analyses [ 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Time–Frequency Analysis of Particulate Matter (PM10) Concentration in Dry Bulk Ports Using the Hilbert–Huang Transform

Feng

Shen

Yang

et al. 2020

IJERPH

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To analyze the time–frequency characteristics of the particulate matter (PM10) concentration, data series measured at dry bulk ports were used to determine the contribution of various factors during different periods to the PM10 concentration level so as to support the formulation of air quality improvement plans around port areas. In this study, the Hilbert–Huang transform (HHT) method was used to analyze the time–frequency characteristics of the PM10 concentration data series measured at three different sites at the Xinglong Port of Zhenjiang, China, over three months. The HHT method consists of two main stages, namely, empirical mode decomposition (EMD) and Hilbert spectrum analysis (HSA), where the EMD technique is used to pre-process the HSA in order to determine the intrinsic mode function (IMF) components of the raw data series. The results show that the periods of the IMF components exhibit significant differences, and the short-period IMF component provides a modest contribution to all IMF components. Using HSA technology for these IMF components, we discovered that the variations in the amplitude of the PM10 concentration over time and frequency are discrete, and the range of this variation is mainly concentrated in the low-frequency band. We inferred that long-term influencing factors determine the PM10 concentration level in the port, and short-term influencing factors determine the difference in concentration data at different sites. Therefore, when formulating PM10 emission mitigation strategies, targeted measures must be implemented according to the period of the different influencing factors. The results of this study can help guide recommendations for port authorities when formulating the optimal layout of measurement devices.

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“…[ 45 ] at NASA. Its primary objective was to facilitate the study and analysis of climate and earth science data by providing a robust time-scale approach for extracting and interpreting valuable information from complex databases and signals [ 46 ].…”

Section: Empirical Mode Decomposition-based Hilbert Huang Transformmentioning

confidence: 99%

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

Gomaa¹,

Zerouali²,

Difi³

et al. 2023

Heliyon

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Forecasting daily flow rate-based intelligent hybrid models combining wavelet and Hilbert–Huang transforms in the mediterranean basin in northern Algeria

Cited by 12 publications

References 49 publications

Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

Time–Frequency Analysis of Particulate Matter (PM10) Concentration in Dry Bulk Ports Using the Hilbert–Huang Transform

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

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