Comparison of support vector regression and extreme gradient boosting for decomposition-based data-driven 10-day streamflow forecasting

Abstract

We recently addressed data-driven monthly streamflow forecasting using a decomposition-based model that relies on Fourier transform (FT) to decompose the monthly streamflow time series into 4 components, with each component comprising of contiguous frequencies and being forecasted independently by support vector regression (SVR). The model, called FT-SVR, is capable of achieving near-perfect monthly inflow forecasting for China’s Three Gorges Dam (TGD). In this paper, we adapt FT-SVR to 10-day streamflow forecasting and the TGD is still taken as the case for study. The 10-day inflow time series is decomposed into 7 components, and each component contains an appropriate small number of contiguous frequencies. We also investigate forecasting each decomposed component by extreme gradient boosting (XGBoost) and the investigated model is accordingly named as FT-XGBoost. XGBoost uses an ensemble of trees as the input–output mapping. The maximum number of iterations for solving convex quadratic programming in SVR is considerably reduced and the parameter calibration process to develop each independent SVR or XGBoost model is enhanced. We compare FT-SVR and FT-XGBoost on three different decomposition strategies: the centered 10-day inflow time series, 4 decomposed components like our previous work, and 7 components. Experimental results on the TGD case study demonstrate that FT-SVR with the decomposition strategy of 7 components is able to derive again near-perfect 10-day streamflow forecasting results, significantly better than FT-SVR with the other decomposition strategies and FT-XGBoost with all the decomposition strategies, while with parameter calibration being performed as efficiently as FT-XGBoost on each decomposition strategy.