# A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting

arXiv: https://arxiv.org/abs/2206.08998 · HTML: https://mlsanity.com/paper/2206.08998/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 218 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 30 of 31)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [NGBoost: Natural Gradient Boosting for Probabilistic Prediction](https://mlsanity.com/paper/1910.03225/) (2019)
- [Distribution Calibration for Regression](https://mlsanity.com/paper/1905.06023/) (2019)
- [Kaggle forecasting competitions: An overlooked learning opportunity](https://mlsanity.com/paper/2009.07701/) (2020)
- [A trans-disciplinary review of deep learning research for water resources scientists](https://mlsanity.com/paper/1712.02162/) (2017)
- [GRATIS: GeneRAting TIme Series with diverse and controllable characteristics](https://mlsanity.com/paper/1903.02787/) (2019)
- [Highly comparative feature-based time-series classification](https://mlsanity.com/paper/1401.3531/) (2014)
- [Using stacking to average Bayesian predictive distributions](https://mlsanity.com/paper/1704.02030/) (2017)
- [Forecasting: theory and practice](https://mlsanity.com/paper/2012.03854/) (2020)
- [Forecast combinations: an over 50-year review](https://mlsanity.com/paper/2205.04216/) (2022)
- [Highly comparative time-series analysis: The empirical structure of time series and their methods](https://mlsanity.com/paper/1304.1209/) (2013)
- [Feature-based time-series analysis](https://mlsanity.com/paper/1709.08055/) (2017)
- [Forecaster's Dilemma: Extreme Events and Forecast Evaluation](https://mlsanity.com/paper/1512.09244/) (2015)
- [Distributional Regression Forests for Probabilistic Precipitation Forecasting in Complex Terrain](https://mlsanity.com/paper/1804.02921/) (2018)
- [Local Linear Forests](https://mlsanity.com/paper/1807.11408/) (2018)
- [Deep Distribution Regression](https://mlsanity.com/paper/1903.06023/) (2019)
- [Boosting algorithms in energy research: A systematic review](https://mlsanity.com/paper/2004.07049/) (2020)
- [Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability](https://mlsanity.com/paper/2001.00811/) (2020)
- [Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale](https://mlsanity.com/paper/2108.00846/) (2021)
- [Marginally-calibrated deep distributional regression](https://mlsanity.com/paper/1908.09482/) (2019)
- [Hydrological post-processing for predicting extreme quantiles](https://mlsanity.com/paper/2202.13166/) (2022)
- [Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale](https://mlsanity.com/paper/1909.00247/) (2019)

## Built on by (most cited first, 12 of 12)

- [A review of predictive uncertainty estimation with machine learning](https://mlsanity.com/paper/2209.08307/) (2022)
- [Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data](https://mlsanity.com/paper/2301.01252/) (2022)
- [Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles](https://mlsanity.com/paper/2302.03606/) (2023)
- [Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale](https://mlsanity.com/paper/2301.01214/) (2022)
- [Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting](https://mlsanity.com/paper/2312.08763/) (2023)
- [Deep Huber quantile regression networks](https://mlsanity.com/paper/2306.10306/) (2023)
- [Ensemble learning for blending gridded satellite and gauge-measured precipitation data](https://mlsanity.com/paper/2307.06840/) (2023)
- [Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products](https://mlsanity.com/paper/2403.10567/) (2024)
- [Uncertainty estimation of machine learning spatial precipitation predictions from satellite data](https://mlsanity.com/paper/2311.07511/) (2023)
- [Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products](https://mlsanity.com/paper/2407.01623/) (2024)
- [Variable transformations in consistent loss functions](https://mlsanity.com/paper/2502.16542/) (2025)
- [Decoding Market Emotions in Cryptocurrency Tweets via Predictive Statement Classification with Machine Learning and Transformers](https://mlsanity.com/paper/2603.24933/) (2026)

## Abstract (arXiv)

Probabilistic forecasting is receiving growing attention nowadays in a variety of applied fields, including hydrology. Several machine learning concepts and methods are notably relevant towards addressing the major challenges of formalizing and optimizing probabilistic forecasting implementations, as well as the equally important challenge of identifying the most useful ones among these implementations. Nonetheless, practically-oriented reviews focusing on such concepts and methods, and on how these can be effectively exploited in the above-outlined essential endeavour, are currently missing from the probabilistic hydrological forecasting literature. This absence holds despite the pronounced intensification in the research efforts for benefitting from machine learning in this same literature. It also holds despite the substantial relevant progress that has recently emerged, especially in the field of probabilistic hydrological post-processing, which traditionally provides the hydrologists with probabilistic hydrological forecasting implementations. Herein, we aim to fill this specific gap. In our review, we emphasize key ideas and information that can lead to effective popularizations, as such an emphasis can support successful future implementations and further scientific developments. In the same forward-looking direction, we identify open research questions and propose ideas to be explored in the future.