Streamflow estimation by support vector machine coupled with different methods of time series decomposition in the upper reaches of Yangtze River, China

Machine learning models combined with time series decomposition are widely employed to estimate streamflow, yet the effect of the utilization of different decomposing methods on estimating accuracy is inadequately investigated and compared. In this paper, the main objective is to research the predictability of monthly streamflow using support vector machine model coupled with discrete wavelet transform (DWT) and empirical mode decomposition (EMD). The influence of the noise component of the decomposed time series on the forecast accuracy is also discussed here. Performance is evaluated through an application on Jinsha River, which is located in the upper reaches of Yangtze River in China. Results indicate that both time series decomposition techniques EMD and DWT contribute to improving the accuracy of streamflow prediction, and deeper comparative analysis shows models coupled with DWT have better prediction capabilities than models coupled with EMD. Furthermore, the high frequency component of the original series is indispensable for high-precision streamflow prediction, which is obvious in flood season.