Multi-Variables-Driven Model Based on Random Forest and Gaussian Process Regression for Monthly Streamflow Forecasting

Due to the inherent non-stationary and nonlinear characteristics of original streamflow and the complicated relationship between multi-scale predictors and streamflow, accurate and reliable monthly streamflow forecasting is quite difficult. In this paper, a multi-scale-variables-driven streamflow forecasting (MVDSF) framework was proposed to improve the runoff forecasting accuracy and provide more information for decision-making. This framework was realized by integrating random forest (RF) and Gaussian process regression (GPR) with multi-scale variables (hydrometeorological and climate predictors) as inputs and is referred to as RF-GPR-MV. To validate the effectiveness and superiority of the RF-GPR-MV model, it was implemented for multi-step-ahead monthly streamflow forecasts with horizons of 1 to 12 months for two key hydrological stations in the Jinsha River basin, Southwest China. Other MVDSF models based on the Pearson correlation coefficient (PCC) and GPR with/without multi-scale variables or the PCC and a backpropagation neural network (BP) or general regression neural network (GRNN), with only previous streamflow and precipitation, namely, PCC-GPR-MV, PCC-GPR-QP, PCC-BP-QP, and PCC-GRNN-QP, respectively, were selected as benchmarks. Experimental results indicated that the proposed model was superior to the other benchmark models in terms of the Nash–Sutcliffe efficiency (NSE) for almost all forecasting scenarios, especially for forecasting with longer lead times. Additionally, the results also confirmed that the addition of large-scale climate and circulation factors was beneficial for promoting the streamflow forecasting ability, with an average contribution rate of about 15%. The RF in the MVDSF framework improved the forecasting performance, with an average contribution rate of about 25%. This improvement was more pronounced when the lead time exceeded 3 months. Moreover, the proposed model could also provide prediction intervals (PIs) to characterize forecast uncertainty, as supplementary information to further help decision makers in relevant departments to avoid risks in water resources management