Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms

Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved. Here, we propose super learning (a type of ensemble learning) by combining 10 machine learning algorithms. We apply the proposed algorithm in one-step-ahead forecasting mode. For the application, we exploit a big dataset consisting of 10-year long time series of daily streamflow, precipitation and temperature from 511 basins. The super ensemble learner improves over the performance of the linear regression algorithm by 20.06%, outperforming the “hard to beat in practice” equal weight combiner. The latter improves over the performance of the linear regression algorithm by 19.21%. The best performing individual machine learning algorithm is neural networks, which improves over the performance of the linear regression algorithm by 16.73%, followed by extremely randomized trees (16.40%), XGBoost (15.92%), loess (15.36%), random forests (12.75%), polyMARS (12.36%), MARS (4.74%), lasso (0.11%) and support vector regression (− 0.45%). Furthermore, the super ensemble learner outperforms exponential smoothing and autoregressive integrated moving average (ARIMA). These latter two models improve over the performance of the linear regression algorithm by 13.89% and 8.77%, respectively. Based on the obtained large-scale results, we propose super ensemble learning for daily streamflow forecasting.