Using Hybrid Wavelet-Exponential Smoothing Approach for Streamflow Modeling

Considering the three intrinsic components (of autoregressive, seasonality, and error) of streamflow time series, the overall performance of the streamflow modeling tool is associated with the correct estimation of these components. In this study, a new hybrid method based on the wavelet transform (WT) as a multiresolution forecasting tool and exponential smoothing (ES) method, with two presented scenarios (WES1 and WES2), was introduced. To this end, the performance of the proposed method was investigated versus four conventional methods of the autoregressive integrated moving average (ARIMA), ES ad-hoc, artificial neural network (ANN), and wavelet-ANN (WANN) for daily and monthly streamflow modeling of West Nishnabotna and Trinity River watersheds with different hydro-geomorphological conditions. In the presented WES technique, firstly, WT is employed for decomposing the observed signal to one approximation (deterministic trend) and more diverse components of subseries (each at a specific frequency). Then, for the first scenario (WES1), only two subseries are introduced to the model as input parameters; however, for the second scenario (WES2), decomposed subseries are separately used as the inputs of ES models. The obtained results indicated that combining WT with the ES method and ANN led to more accurate modeling. The proposed methodology (WES2) that used all decomposed subseries separately improved the efficiency of models up to 30% and 10% for the daily dataset and up to 88% and 57% for the monthly dataset, respectively, for the West Nishnabotna and Trinity Rivers.

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