Development of a new method of wavelet aided trend detection and estimation

By K. Adamowski, A. Prokoph, Jan Adamowski. 2009

149 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3ff791a6fceca66d804317c57e4935d267b428fa

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008) (2012, background): “The wavelet transform can handle these issues by decomposing a one-dimensional signal into two-dimensional time–frequency domains at the same time (Adamowski et al., 2009).”
Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. (2010, background): “…1999), to characterize daily streamflow (Smith et al., 1998; Saco and Kumar, 2000) and monthly reservoir inflow (Coulibaly et al., 2000), to evaluate rainfall–runoff models (Lane, 2007), to analyze streamflow trends (Adamowski et al., 2009), and to forecast river flow (Adamowski, 2007, 2008a,b).”
Discrete wavelet‐based trend identification in hydrologic time series (2013, methodology): “…de Artigas et al. (2006) used DWT to assess trends in geomagnetic activity; and Almasri et al. (2008) used DWT to assess trends in temperature; Adamowski et al. (2009) proposed a continuous wavelet-aided method for trend identification in monthly minimum streamflow in five different eco-zones…”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “Hence, wavelet decomposition of inputs is considered a multi-resolution tool for pre-processing non-stationary signals; for instance, prior to developing ML models (Adamowski et al. 2012) and analysing trends or periodicities (Adamowski et al. 2009; Nalley et al. 2012, 2013).”
Assessment of drought trend and variability in India using wavelet transform (2020, methodology): “A wide range of literature is available on WT and its application in the field of hydrology, for example, it is discussed in detail in Grossmann and Morlet (1984), Mallat (1989), Daubechies (1990) and Torrence and Compo (1998), while its applications in hydrology and meteorology are discussed in Nas”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, background): “sub-signals at different temporal scales (levels) is helpful in better interpreting hydrological processes (Adamowski, 2008a,b; Adamowski et al., 2009; Kisi, 2010; Mirbagheri et al., 2010; Nason and Sachs, 1999; Sang, 2012).”