Ensemble learning for predictive uncertainty estimation with application to the correction of satellite precipitation products

2024-03-14

AI Summary

This study addresses the challenge of calibrating satellite-based precipitation products to improve uncertainty quantification in probabilistic spatial rainfall forecasting. We propose an ensemble framework integrating multiple quantile regression models, systematically evaluating nine quantile learners—including Quantile Regression (QR), Quantile Random Forest (QRF), Generalized Random Forest (GRF), Gradient Boosting Machine (GBM), LightGBM, and Quantile Recurrent Neural Network (QRNN)—alongside six ensemble strategies and three simple combination methods. A novel lightweight feature engineering approach is introduced, incorporating geographic distance-weighted satellite precipitation and elevation features. Experiments on 15 years of monthly data across the Continental United States (CONUS) demonstrate that QR- and QRNN-based ensembles achieve average improvements of 3.91%–8.95% in predictive accuracy within the 0.025–0.975 quantile interval, significantly outperforming single-model quantile regression baselines. These results validate the efficacy and robustness of multi-model quantile ensembling for probabilistic precipitation prediction.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Predictions in the form of probability distributions are crucial for effective decision-making. Quantile regression enables such predictions within spatial prediction settings that aim to create improved precipitation datasets by merging remote sensing and gauge data. However, ensemble learning of quantile regression algorithms remains unexplored in this context and, at the same time, it has not been substantially developed so far in the broader machine learning research landscape. Here, we introduce nine quantile-based ensemble learners and address the afore-mentioned gap in precipitation dataset creation by presenting the first application of these learners to large precipitation datasets. We employed a novel feature engineering strategy, reducing predictors to distance-weighted satellite precipitation at relevant locations, combined with location elevation. Our ensemble learners include six ensemble learning and three simple methods (mean, median, best combiner), combining six individual algorithms: quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM), and quantile regression neural networks (QRNN). These algorithms serve as both base learners and combiners within different ensemble learning methods. We evaluated performance against a reference method (QR) using quantile scoring functions in a large dataset comprising 15 years of monthly gauge-measured and satellite precipitation in the contiguous United States (CONUS). 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.

Problem

• Quantile Regression
• Satellite Data
• Rainfall Prediction

Innovation

• Ensemble Methods
• Precipitation Prediction
• Satellite Data Integration