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.

I am very grateful to Georgia Papacharalampous and Hristos Tyralis for conceiving the idea of the study and carrying out most of the work (under the supervision of Demetris Koutsoyiannis). I am enjoying working with them a lot.

Thank you to your interest!
Best,
Alberto

Download the paper