Assessment of neuro-fuzzy approach based different wavelet families for daily flow rates forecasting

Abda, Zaki; Chettih, Mohamed; Zerouali, Bilel

doi:10.1007/s40808-020-00855-1

Cited by 15 publications

(2 citation statements)

References 35 publications

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“…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%

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: New Insights From the Proposed Hybrid Methodsmentioning

confidence: 99%

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

et al. 2022

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“…Nevertheless, most research has focused on using DWT with non-parametric statistical tests. Quite the opposite when predicting the future hydroclimatic parameters, researchers have been creative in using time−frequency-based approaches with various types of artificial intelligence algorithms, such as neural network, neuro−fuzzy approach, and extreme learning machine [85][86][87], but this application is still in its beginning stage, which opens the way for more development in the future.…”

Section: Combining Triangular Trend Analysis Methodology With Discret...mentioning

confidence: 99%

An Enhanced Innovative Triangular Trend Analysis of Rainfall Based on a Spectral Approach

Zerouali

Al‐Ansari

Chettih

et al. 2021

Water

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The world is currently witnessing high rainfall variability at the spatiotemporal level. In this paper, data from three representative rain gauges in northern Algeria, from 1920 to 2011, at an annual scale, were used to assess a relatively new hybrid method, which combines the innovative triangular trend analysis (ITTA) with the orthogonal discrete wavelet transform (DWT) for partial trend identification. The analysis revealed that the period from 1950 to 1975 transported the wettest periods, followed by a long-term dry period beginning in 1973. The analysis also revealed a rainfall increase during the latter decade. The combined method (ITTA–DWT) showed a good efficiency for extreme rainfall event detection. In addition, the analysis indicated the inter- to multiannual phenomena that explained the short to medium processes that dominated the high rainfall variability, masking the partial trend components existing in the rainfall time series and making the identification of such trends a challenging task. The results indicate that the approaches—combining ITTA and selected input combination models resulting from the DWT—are auspicious compared to those found using the original rainfall observations. This analysis revealed that the ITTA–DWT method outperformed the ITTA method for partial trend identification, which proved DWT’s efficiency as a coupling method.

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WGCN: A Novel Wavelet Graph Neural Network for Metro Ridership Prediction

Tang

Zhang

Jin

et al. 2023

Lecture Notes in Computer Science

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Assessment of neuro-fuzzy approach based different wavelet families for daily flow rates forecasting

Cited by 15 publications

References 35 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

An Enhanced Innovative Triangular Trend Analysis of Rainfall Based on a Spectral Approach

WGCN: A Novel Wavelet Graph Neural Network for Metro Ridership Prediction

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