Wavelet regression model for short-term streamflow forecasting

Abstract

Summary Wavelet regression (WR) technique is proposed for short-term streamflow forecasting in this study. The WR model is improved combining two methods, discrete wavelet transform and linear regression model. The proposed model is applied to the daily streamflow data of two stations, Karabuk and Derecikviran, on the Filyos River in the Western Black Sea region of Turkey. Root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) statistics are used for evaluating the accuracy of the WR models. In the first part of the study, the accuracy of the WR models is compared with the artificial neural network (ANN) and autoregressive moving average (ARMA) models in 1-day ahead streamflow forecasting. Comparison results reveal that the WR model performs better than the ANN and ARMA models. The ARMA model is also found to be slightly better than the ANN. For the Karabuk and Derecikviran stations, it was found that WR models with RMSE = 8.48 m3/s, MAE = 2.46 m3/s, R = 0.978 and RMSE = 33.3 m3/s, MAE = 10.2 m3/s, R = 0.976 in the validation stage are superior in forecasting 1-day ahead streamflows than the most accurate ARMA models with RMSE = 13.5 m3/s, MAE = 3.44 m3/s, R = 0.942 and RMSE = 46.5 m3/s, MAE = 13.2 m3/s, R = 0.953, respectively. In the second part of study, WR and ANN models are compared in 2- and 3-day ahead streamflow forecasting. Based on the comparison results, WR models are found to be more accurate than the ANN models.