Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting

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A hybrid support vector regression framework for streamflow forecast (2019, methodology): “By stretching and translating, the signal can be multi-scale analyzed, therefore, it is suitable for the analysis of non-stationary hydrological time series (Guo et al., 2011; Kisi, 2011; Liu et al., 2014).”
Monthly runoff forecasting based on LSTM–ALO model (2018, methodology): “Application of wavelet analysis in improving monthly streamflow forecasting performance of the SVM was studied by Kalteh and Chen (2014).”
Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, background): “Over the past few decades, EC and W-AI models have been greatly dedicated to the river flow forecasting problems (Dumedah 2015; Friedel 2011; Kisi 2010; Liu et al. 2014; Maier et al. 2014; Mehr et al. 2013; Nourani et al. 2011; Tran et al. 2015; Whigham and Crapper 2001; Zimmer et al. 2015).”
A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction (2019, methodology): “To date, a large number of data-driven models have been applied and utilized for the aforementioned purpose (Liu et al. 2014).”