Recent advances in wavelet analyses: Part 1. A review of concepts

By David Labat. 2005

639 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/6c2b847787f8644944b6bd1e2c523d342f96d9bc

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In Geophysics

Citations in the corpus

Evapotranspiration modeling using a wavelet regression model (2010, methodology): “In the last decade, wavelet transform has become a useful technique for analyzing variations, periodicities and trends in time series (Labat et al. 2000; Smith et al. 1998; Lu 2002; Chou and Wang 2002; Xingang et al. 2003; Coulibaly and Burn 2004; Labat 2005; Labat et al. 2005; Partal and Küçük 2006”
A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling (2009, background): “A comprehensive literature survey of wavelet in geosciences can be found in Foufoula-Georgiou and Kumar (1995) and the most recent contributions are cited by Labat (2005) .”
Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting (2011, methodology): “ Labat (2005) reviewed the most recent wavelet applications in the field of earth sciences and illustrated new wavelet analysis methods (combined multiresolution-continuous wavelet analysis method, wavelet entropy) in the field of hydrology.”
Discrete wavelet‐based trend identification in hydrologic time series (2013, background): “Several studies concerning the issue were conducted, and various methods for trend identification can be found (Kahya and Kalayci, 2004; Macdonald et al., 2005; Xu et al., 2007; Adam and Lettenmaier, 2008).”
Trend analysis using discrete wavelet transform (DWT) for long-term precipitation (1851–2006) over India (2017, methodology): “In order to analyse the contributing events for extreme events, the method of wavelet analysis is very valuable to decompose the original time series into different frequencies, retaining information in the time domain (Labat 2005, Chou 2011, Nalley et al. 2013).”
Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review (2019, background): “However, the observed hydrologic series are usually complex and show non-stationary and multi-temporal scale characteristics in daily, monthly, annual, inter-annual, decadal and larger scales [3,77,78].”
A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation (2009, background): “A comprehensive literature survey of wavelet in geosciences can be found in FoufoulaGeorgiou and Kumar (1995) and the most recent contributions are cited by Labat (2005).”