Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting
Problem
ML-based ionosphere and space weather forecasts lacked uncertainty quantification, so the confidence and reliability of their results were unknown.
Why it matters
Without uncertainty estimates, users cannot know how confident and reliable ML forecast results are.
What the AI did
Implemented and compared four uncertainty quantification approaches (super-ensemble, quantile gradient boosting, Bayesian neural network, BNN with data uncertainty) for 1-day-ahead VTEC forecasting with 95% confidence intervals.
Results
1-day-ahead VTEC forecasts with 95% CIs; techniques considering only model parameter uncertainties (super-ensemble, BNN) predicted narrow CIs and over-optimistic results
Impact
The evidence supports a comparative finding on which uncertainty methods yield reliable confidence estimates for ML space-weather forecasts.
Record
| Venue | Space Weather |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Randa Natraš, Benedikt S. Soja, Michael F. Schmidt |
| Citations | 17 (OpenAlex, at import time) |
| References | 55 |
| Model or method | Bayesian neural networks, quantile gradient boosting, super-ensemble of ML models |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1029/2023sw003483 |
| Open access | gold |
| Keywords | space weather, ionosphere, VTEC, uncertainty quantification, Bayesian neural networks |
Importance rating
real but narrower advance
Uncertainty quantification for ML ionosphere/space-weather forecasts is a real but narrow methodological advance; abstract gives no scale of affected users or systems.
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