Comparative Study of Multi-Combination Models for Medium- and Long-Term Runoff Prediction in Weihe River

The characteristics of hydrological data include nonconsistency and nonlinearity. The prediction accuracy can be improved through the combination of both the decomposition algorithm and the runoff model. Previous studies have typically focused on the combination of a single decomposition algorithm and model. These studies have compared the prediction accuracy before and after decomposition, ignoring the role of multiple decomposition algorithms and models. Considering the limitations of previous single combinations of decomposition algorithms and models, this study will explore the unique features of hydrological data by using a combination of five algorithms, including Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), TIME series decomposition (TIME), Variational Mode Decomposition (VMD), and Singular Spectrum Analysis (SSA). The study constructed models for Prophet, Long Short-Term Memory (LSTM), Multiple Regression (MLR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR). Thirty combined prediction models were then developed and used to forecast medium and long-term runoff at Xianyang Station. To comprehensively evaluate the forecasted runoff results, multiple evaluation metrics were used. The prediction accuracy improved after using EMD and TIME decomposition, but the difference was insignificant, and TIME decomposition was the least effective. VMD, EEMD, and SSA, on the other hand, yielded higher data quality. The combined model achieved an NSE above 0.70, demonstrating good prediction results. Of the thirty combined models, the SSA-SVR and SSA-LSTM models were most accurate, with a verification NSE of 0.90. This study developed a comprehensive, reliable, and accurate combination prediction model by employing multiple decomposition algorithms and models. These findings provide a framework for characteristics-driven watershed runoff prediction and water resources scheduling.