A review on the applications of wavelet transform in hydrology time series analysis

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Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review (2019, methodology): “In addition, only some articles, such as Sang et al. [92], discussed the advantages and disadvantages of DWT and CWT.”
Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting (2014, methodology): “It is thus possible to generate better forecasts by combining the strengths of wavelet transform and SVR (or other data-driven models) (Kisi, 2008, 2009; Nourani et al., 2009, 2011; Remesan et al., 2009; Adamowski and Sun, 2010; Pramanik et al., 2010; Shiri and Kisi, 2010; Li, 2011; Tiwari and Chatt”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “In a review of the applications of the wavelet transform in hydrologic time series modeling, Sang (2013a) highlighted the multifaceted information that can be drawn from such analysis: characterization and understanding of hydrologic series’ multi-temporal scales, identification of seasonalities…”