This paper investigates the performance of wavelet-based regression models for monthly streamflow forecasting. The wavelet-based regression model combines wavelet transformation and multiple linear regression (LR). The wavelet-based regression forecasts are also compared to the wavelet-based neural network, which combines the wavelet transformation and feed forward neural network. The wavelet transformation has significantly positive effects on the modeling performance. In this study, the different approaches of the wavelet-based models were applied to forecast the monthly flow. The results show that the wavelet-based feed forward neural network and the wavelet-based linear regression (WLR) produce very good results for 1-month-ahead streamflow forecasting. Both techniques demonstrated an almost similar performance. Also, the result of the WLR5 model is better than the results of the other WLR models in terms of performance criteria.
Research Article|August 20 2016
Wavelet regression and wavelet neural network models for forecasting monthly streamflow
Turgay Partal; Wavelet regression and wavelet neural network models for forecasting monthly streamflow. Journal of Water and Climate Change 1 March 2017; 8 (1): 48–61. doi: https://doi.org/10.2166/wcc.2016.091
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