@article{3005443,
    title = "Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS",
    author = "Tyralis, H. and Papacharalampous, G. and Burnetas, A. and Langousis, A.",
    journal = "Journal of Hydrologic Engineering",
    year = "2019",
    volume = "577",
    publisher = "Elsevier B.V.",
    issn = "1084-0699",
    doi = "10.1016/j.jhydrol.2019.123957",
    keywords = "Climate models;  Forecasting;  Forestry;  Hydrology;  Machine learning;  Regression analysis, Ensemble learning;  Hydrological uncertainty;  Interval score;  Probabilistic forecasts;  Quantile regression, Learning algorithms, algorithm;  forecasting method;  hydrological modeling;  machine learning;  simulation;  stacking;  uncertainty analysis, United States",
    abstract = "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. © 2019 Elsevier B.V."
}