Performance enhancement of SVM model using discrete wavelet transform for daily streamflow forecasting

Abstract

Streamflow modeling becomes a vital task in any hydrological study for an improved planning and management of water resources. Soft computing and machine learning techniques are becoming popular day by day for their predictive capability when limited input data are available. In the present study, Support Vector Machine (SVM) technique is applied to forecast 1-day, 3-day, and 5-day ahead streamflow using daily streamflow time-series of Khanapur, Cholachguda, and Navalgund gauging stations in Malaprabha sub-basin located in the Karnataka state of India. Furthermore, Discrete Wavelet Transform is used as a data pre-processing method to evaluate the performance enhancement of SVM model, for which four different mother wavelet functions are used and tested separately, namely, Haar, Daubechies, Coiflets, and Symlets. Models are evaluated using coefficient of determination (R2), root-mean-square error, and Nash–Sutcliffe efficiency. The study indicates that the performance of SVM model improves considerably when wavelet method is coupled. It is found that the R2 values for Khanapur station using SVM are 0.91, 0.66, and 0.46 for 1-day, 3-day, and 5-day lead-time forecasts, respectively. However, when wavelet method is coupled with SVM model, the R2 is improved to 0.99, 0.73, and 0.68 for 1-day, 3-day, and 5-day lead-time forecasts, respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.