# Ensemble learning for blending gridded satellite and gauge-measured precipitation data

arXiv: https://arxiv.org/abs/2307.06840 · HTML: https://mlsanity.com/paper/2307.06840/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 123 references that are in the map.
- First posted on arXiv 9 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [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)
- [Using stacking to average Bayesian predictive distributions](https://mlsanity.com/paper/1704.02030/) (2017)
- [Forecast combinations: an over 50-year review](https://mlsanity.com/paper/2205.04216/) (2022)
- [A review of predictive uncertainty estimation with machine learning](https://mlsanity.com/paper/2209.08307/) (2022)
- [The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling](https://mlsanity.com/paper/1403.1452/) (2014)
- [Boosting algorithms in energy research: A systematic review](https://mlsanity.com/paper/2004.07049/) (2020)
- [A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting](https://mlsanity.com/paper/2206.08998/) (2022)
- [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)
- [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)

## Built on by (most cited first, 3 of 3)

- [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)

## Abstract (arXiv)

Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables. Alongside this, it is increasingly recognised in many fields that combinations of algorithms through ensemble learning can lead to substantial predictive performance improvements. Still, a sufficient number of ensemble learners for improving the accuracy of satellite precipitation products and their large-scale comparison are currently missing from the literature. In this study, we work towards filling in this specific gap by proposing 11 new ensemble learners in the field and by extensively comparing them. We apply the ensemble learners to monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets that span over a 15-year period and over the entire the contiguous United States (CONUS). We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The ensemble learners combine the predictions of six machine learning regression algorithms (base learners), namely the multivariate adaptive regression splines (MARS), multivariate adaptive polynomial splines (poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN), and each of them is based on a different combiner. The combiners include the equal-weight combiner, the median combiner, two best learners and seven variants of a sophisticated stacking method. The latter stacks a regression algorithm on top of the base learners to combine their independent predictions...