Scientific paper: Probabilistic Hydrological Post-Processing
The scientific paper: Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms, published on Water, is available open access at the link below.
The study focuses on the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing in order to derive uncertainty of hydrological simulations.




