A comparative study of models for short-term streamflow forecasting with emphasis on wavelet-based approach

Skilful short-term streamflow forecasting is a challenging task, but useful for addressing a variety of issues associated with water resources planning and management. This study investigates the performance of four popular models for short-term streamflow forecasting, with particular emphasis on the combination of each model with wavelet transform (WT). The autoregressive (AR) model, autoregressive moving average (ARMA) model, artificial neural network (ANN) model, and linear regression (LR) model are used as the base models, and WT-AR, WT-ARMA, WT-ANN, and WT-LR models are used as the composite or hybrid models. These eight models are applied for short-term forecasting of daily streamflow time series in two different river basins in China. Streamflow data from two stations (Yingluoxia and Zhamashike) in the Heihe River basin of North China and two stations (Wuzhou and Longchuan) in the Pearl River basin of South China are considered, and forecasts are made 1-day, 2-day, and 3-day ahead. The accuracy of the models is evaluated using three widely used performance measures: root mean square error, mean absolute error, and correlation coefficient (R). The performances of the eight models are compared, and the influencing role of the wavelet transform in the hybrid models is discussed. The results suggest that the WT-ANN and ANN models are more suitable for the northern basin but perform poorly for the southern basin, while the WT-ARMA model is more suitable for the southern basin. The results also suggest that the wavelet-based hybrid models are better than the single models particularly for longer lead times, offering gradual improvement with increasing lead time. Therefore, the wavelet-based models are especially useful for areas that suffer from frequent natural disasters, fragile ecological environment, and poor self-regulations.