Summary of Ensemble Learning For Predictive Uncertainty Estimation with Application to the Correction Of Satellite Precipitation Products, by Georgia Papacharalampous et al.
Ensemble learning for predictive uncertainty estimation with application to the correction of satellite precipitation products
by Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a novel approach to predicting precipitation datasets by merging remote sensing and gauge data. Quantile regression is used as a crucial tool for making probability distributions, enabling effective decision-making. However, the paper introduces a gap in this context by applying ensemble learning of quantile regression algorithms to large precipitation datasets. The authors employ a feature engineering strategy to reduce predictors to distance-weighted satellite precipitation at relevant locations combined with location elevation. Nine quantile-based ensemble learners are introduced, including six ensemble learning methods and three simple methods (mean, median, best combiner). The algorithms serve as both base learners and combiners within different ensemble learning methods. Evaluation of performance against a reference method (quantile regression) using quantile scoring functions in a large dataset shows that ensemble learning with QR and QRNN yielded the best results across quantile levels ranging from 0.025 to 0.975, outperforming the reference method by 3.91% to 8.95%. This demonstrates the potential of ensemble learning to improve probabilistic spatial predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new approach for predicting precipitation datasets by merging remote sensing and gauge data. It uses quantile regression to make probability distributions. The researchers show how combining multiple algorithms can lead to better results. They used a special way of preparing data that combines satellite data with location elevation information. Nine different ways of combining these algorithms are tested, and the best ones are found to be using QR and QRNN. These methods outperformed the usual method by a lot. |
Keywords
* Artificial intelligence * Feature engineering * Probability * Regression
