Enhanced machine learning model via twin support vector regression for streamflow time series forecasting of hydropower reservoir
Xinyue Fu, Zhong-kai Feng, Hui Cao, Baofei Feng, Zheng-yu Tan, Yinshan Xu, Wenjing Niu
Abstract
The non-stationary, complex, and non-linear characteristics of streamflow time series have a significant impact on the simulation results of the conventional hydrological forecasting models. To improve the performances, this paper develops an enhanced machine learning model for streamflow time series forecasting, where the twin support vector regression (TSVR) is combined with singular spectrum analysis (SSA) and grey wolf optimizer (GWO). Specially, the SSA method is set as the data preprocessing tool for pattern identification; the TSVR model is set as the basic forecasting module for each pattern and the GWO method is used as the optimizer to select feasible parameter combination. Multi-step-ahead streamflow forecasting tasks are used to examine the feasibility and predictability of the proposed model. The results indicate that the proposed model can yield superior results compared with the traditional forecasting models. Thus, a robust and reliable tool is provided for streamflow time series forecasting under uncertainty.