  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 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%).
Based on the obtained large-scale results, we propose super learning for daily
streamflow forecasting.
