Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting
This study investigated the performance and potential of a hybrid model that combined the discrete wavelet transform and support vector regression (the DWT-SVR model) for daily and monthly streamflow forecasting. Three key factors of the wavelet decomposition phase (mother wavelet, decomposition lev...
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| 1. Verfasser: | |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
6 July 2014
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| In: |
Journal of hydrology
Year: 2014, Jahrgang: 519, Pages: 2822-2831 |
| ISSN: | 1879-2707 |
| DOI: | 10.1016/j.jhydrol.2014.06.050 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.jhydrol.2014.06.050 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S0022169414005101 
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| Verfasserangaben: | Zhiyong Liu, Ping Zhou, Gang Chen, Ledong Guo |
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| 520 | |a This study investigated the performance and potential of a hybrid model that combined the discrete wavelet transform and support vector regression (the DWT-SVR model) for daily and monthly streamflow forecasting. Three key factors of the wavelet decomposition phase (mother wavelet, decomposition level, and edge effect) were proposed to consider for improving the accuracy of the DWT-SVR model. The performance of DWT-SVR models with different combinations of these three factors was compared with the regular SVR model. The effectiveness of these models was evaluated using the root-mean-squared error (RMSE) and Nash-Sutcliffe model efficiency coefficient (NSE). Daily and monthly streamflow data observed at two stations in Indiana, United States, were used to test the forecasting skill of these models. The results demonstrated that the different hybrid models did not always outperform the SVR model for 1-day and 1-month lead time streamflow forecasting. This suggests that it is crucial to consider and compare the three key factors when using the DWT-SVR model (or other machine learning methods coupled with the wavelet transform), rather than choosing them based on personal preferences. We then combined forecasts from multiple candidate DWT-SVR models using a model averaging technique based upon Akaike’s information criterion (AIC). This ensemble prediction was superior to the single best DWT-SVR model and regular SVR model for both 1-day and 1-month ahead predictions. With respect to longer lead times (i.e., 2- and 3-day and 2-month), the ensemble predictions using the AIC averaging technique were consistently better than the best DWT-SVR model and SVR model. Therefore, integrating model averaging techniques with the hybrid DWT-SVR model would be a promising approach for daily and monthly streamflow forecasting. Additionally, we strongly recommend considering these three key factors when using wavelet-based SVR models (or other wavelet-based forecasting models). | ||
| 650 | 4 | |a Indiana | |
| 650 | 4 | |a Model averaging | |
| 650 | 4 | |a Streamflow forecasting | |
| 650 | 4 | |a Support vector regression | |
| 650 | 4 | |a Wavelet analysis | |
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