Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS

摘要

Post-processing of hydrological model simulations using machine learning algorithms can be applied to quantify the uncertainty of hydrological predictions. Combining multiple diverse machine learning algorithms (referred to as base-learners) using stacked generalization (stacking, i.e. a type of ensemble learning) is considered to improve predictions relative to the base-learners. Here we propose stacking of quantile regression and quantile regression forests. Stacking is performed by minimising the interval score of the quantile predictions provided by the ensemble learner, which is a linear combination of quantile regression and quantile regression forests. The proposed ensemble learner post-processes simulations of the GR4J hydrological model for 511 basins in the contiguous US. We illustrate its significantly improved performance relative to the base-learners used and a less prominent improvement relative to the "hard to beat in practice" equal-weight combiner.