Developing hydrologic models based on data-driven approaches (DDA) is very complicated due to the complex nature of meteorological data. For example, a high degree of irregularities, periodicities, jumps, and other forms of stochastic behavior influence the accuracy of river flow forecasting. In this study, M5 model tree (M5Tree) and multivariate adaptive regression spline (MARS) models were developed to forecast one and multi-day-ahead river flow. Moreover, ensemble empirical mode decomposition (EEMD), a robust data pre-processing technique, was used to enhance M5Tree and MARS models’ forecasting. Also, Mallows’ coefficient (C P ), one of the procedures to determine the input variables, was used to obtain the optimum values of hydrological time series. The developed models were validated using two different meteorological stations (e.g., Kordkheyl in Iran and Hongcheon in South Korea). Forecasting performance of developed models (e.g., M5Tree, MARS, EEMD-M5Tree, and EEMD-MARS) was evaluated using six different statistical criteria. Comparing the results between standalone and hybrid models indicated that a data pre-processing technique can enhance the performance of standalone models (e.g., M5Tree and MARS). EEMD-MARS model (NSE = 0.819 and RMSE = 7.206 m 3 /s (Kordkheyl station) and NSE = 0.738 and RMSE = 50.426 m 3 /s (Hongcheon station)) outperformed M5Tree, MARS, and EEMD-M5Tree models based on two-day-ahead river flow forecasting in validation stage, respectively. Results showed that EEMD-MARS model was an efficient and robust tool to forecast one and multi-day-ahead (e.g., two, three, and four-day-ahead) river flow.
- Ensemble empirical mode decomposition
- M5 model tree
- Mallows’ coefficient (C )
- Multivariate adaptive regression spline
- River flow forecasting