GSST/CBERS-5 International Symposium on Machine Learning in Heliophysics and Space Weather

Explainable AI for Operational Space Weather Forecasting: An Interpretable Prototype for the EMBRACE-INPE Monitoring Network

Not scheduled

20m

Fernando de Mendonça - LIT (National Institute for Space Research, São José dos Campos, SP, Brazil)

Oral Space Weather Forecasting & Operations Oral Contributions

Speaker

Reinaldo Rosa (National Institute for Space Research (INPE))

Description

Operational space weather monitoring requires not only accurate forecasts but also transparent and interpretable decision support tools. We present an Explainable Artificial Intelligence (XAI) prototype designed for the EMBRACE-INPE environment. The system integrates GOES X-ray flux, SYM-H index, and AE index, capturing the causal propagation from solar activity to geomagnetic and auroral responses. A sequence-based forecasting module produces six-hour predictions with hyperparameters optimized via Optuna. The XAI layer incorporates uncertainty bands, lag-aware propagation markers, and interpretable multivariate relationships. A three-dimensional regression representation exposes nonlinear dependencies between delayed solar drivers and auroral activity. The prototype is implemented as an interactive dashboard supporting human forecasters. Results illustrate how explainable AI can be embedded directly into operational interfaces, bridging machine learning forecasts and human-centered situational awareness.