Assessment of hybrid kernel function in extreme support vector regression model for streamflow time series forecasting based on a bayesian estimator decomposition algorithm

Diverse decomposition algorithms have been widely employed to streamflow time series forecasting. Their applications, however, are hindered by the plausible high accuracy in the overall decomposition-based framework. This paper firstly introduces a novel decomposition algorithm named Bayesian estimator of abrupt change, seasonality and trend (BEAST) into streamflow forecasting to alleviate the boundary effect. Practical samples are generated under the modified two-stage decomposition prediction (TSDP) framework. A hybrid kernel function, which benefits from two different standalone ones, is designed for kernel extreme support vector regression and the HKESVR model is trained on the samples using 10-fold cross-validation strategy. Comparative experiments are conducted on three monthly streamflow series from basins with diverse hydroclimatic conditions. The results in different lead times (1-, 3-, and 5-month-ahead) show that the BEAST algorithm imposes an average improvement of 5.14% and 12.25% for the root-mean-square error and Nash-Sutcliffe efficiency coefficient respectively on the standalone models and shares a comprehensive similar performance on the mean absolute percentage error. And the nonparametric test results reveal that the BEAST method shows a significant improvement on the comprehensive performance compared with a conventional decomposition method. By contrast, the differences between machine learning models are much smaller. The hybrid kernel function works well in some specific cases in which the standalone kernel function fails. The hybrid BEAST-HKESVR is reliable enough to rank the second place among the fifteen tested models. Finally, the effects of hyperparameters in the BEAST algorithm are discussed and relevant suggestions on them are provided.