The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow

Hybrid models that combine wavelet transformation (WT) as a pre-processing tool with data-driven models (DDMs) as modeling approaches have been widely investigated for forecasting streamflow. The WT approach has been applied to original time series for decomposing processes prior to the application...

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Veröffentlicht in:	IEEE access 2020, Vol.8, p.101993-102006
Hauptverfasser:	Hadi, Sinan Jasim, Tombul, Mustafa, Salih, Sinan Q., Al-Ansari, Nadhir, Yaseen, Zaher Mundher
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Continuous wavelet transforms Convolution Data models Decomposition Discrete wavelet transforms Evapotranspiration Experiments extreme learning machine Forecasting Geoteknik gradient boosting Machine learning Mathematical models Modelling Prediction models Predictive models Rainfall Root-mean-square errors Soil Mechanics Stream flow Streamflow forecasting streamflow monitoring Time series Time series analysis wavelet transformation Wavelet transforms
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